Data mining
Overview
 
Data mining a relatively young and interdisciplinary field of computer science
Computer science
Computer science or computing science is the study of the theoretical foundations of information and computation and of practical techniques for their implementation and application in computer systems...

 is the process of discovering new patterns from large data set
Data set
A data set is a collection of data, usually presented in tabular form. Each column represents a particular variable. Each row corresponds to a given member of the data set in question. Its values for each of the variables, such as height and weight of an object or values of random numbers. Each...

s involving methods at the intersection of artificial intelligence
Artificial intelligence
Artificial intelligence is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its...

, machine learning
Machine learning
Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...

, statistics
Statistics
Statistics is the study of the collection, organization, analysis, and interpretation of data. It deals with all aspects of this, including the planning of data collection in terms of the design of surveys and experiments....

 and database system
Database system
A database system is a term that is typically used to encapsulate the constructs of a data model, database Management system and database....

s. The goal of data mining is to extract knowledge from a data set in a human-understandable structure and involves database and data management
Data management
Data management comprises all the disciplines related to managing data as a valuable resource.- Overview :The official definition provided by DAMA International, the professional organization for those in the data management profession, is: "Data Resource Management is the development and execution...

, data preprocessing
Data Pre-processing
Data pre-processing is an often neglected but important step in the data mining process. The phrase "Garbage In, Garbage Out" is particularly applicable to data mining and machine learning projects...

, model
Statistical model
A statistical model is a formalization of relationships between variables in the form of mathematical equations. A statistical model describes how one or more random variables are related to one or more random variables. The model is statistical as the variables are not deterministically but...

 and inference
Statistical inference
In statistics, statistical inference is the process of drawing conclusions from data that are subject to random variation, for example, observational errors or sampling variation...

 considerations, interestingness metrics, complexity
Computational complexity theory
Computational complexity theory is a branch of the theory of computation in theoretical computer science and mathematics that focuses on classifying computational problems according to their inherent difficulty, and relating those classes to each other...

 considerations, post-processing of found structure, visualization
Data visualization
Data visualization is the study of the visual representation of data, meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information"....

 and online updating
Online algorithm
In computer science, an online algorithm is one that can process its input piece-by-piece in a serial fashion, i.e., in the order that the input is fed to the algorithm, without having the entire input available from the start. In contrast, an offline algorithm is given the whole problem data from...

.

The term is a buzzword
Buzzword
A buzzword is a term of art, salesmanship, politics, or technical jargon that is used in the media and wider society outside of its originally narrow technical context....

, and is frequently misused to mean any form of large scale data or information processing (collection, extraction
Information extraction
Information extraction is a type of information retrieval whose goal is to automatically extract structured information from unstructured and/or semi-structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language...

, warehousing
Data warehouse
In computing, a data warehouse is a database used for reporting and analysis. The data stored in the warehouse is uploaded from the operational systems. The data may pass through an operational data store for additional operations before it is used in the DW for reporting.A data warehouse...

, analysis
Data analysis
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making...

 and statistics) but also generalized to any kind of computer decision support system
Decision support system
A decision support system is a computer-based information system that supports business or organizational decision-making activities. DSSs serve the management, operations, and planning levels of an organization and help to make decisions, which may be rapidly changing and not easily specified in...

 including artificial intelligence
Artificial intelligence
Artificial intelligence is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its...

, machine learning
Machine learning
Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...

 and business intelligence
Business intelligence
Business intelligence mainly refers to computer-based techniques used in identifying, extracting, and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes....

.
Encyclopedia
Data mining a relatively young and interdisciplinary field of computer science
Computer science
Computer science or computing science is the study of the theoretical foundations of information and computation and of practical techniques for their implementation and application in computer systems...

 is the process of discovering new patterns from large data set
Data set
A data set is a collection of data, usually presented in tabular form. Each column represents a particular variable. Each row corresponds to a given member of the data set in question. Its values for each of the variables, such as height and weight of an object or values of random numbers. Each...

s involving methods at the intersection of artificial intelligence
Artificial intelligence
Artificial intelligence is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its...

, machine learning
Machine learning
Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...

, statistics
Statistics
Statistics is the study of the collection, organization, analysis, and interpretation of data. It deals with all aspects of this, including the planning of data collection in terms of the design of surveys and experiments....

 and database system
Database system
A database system is a term that is typically used to encapsulate the constructs of a data model, database Management system and database....

s. The goal of data mining is to extract knowledge from a data set in a human-understandable structure and involves database and data management
Data management
Data management comprises all the disciplines related to managing data as a valuable resource.- Overview :The official definition provided by DAMA International, the professional organization for those in the data management profession, is: "Data Resource Management is the development and execution...

, data preprocessing
Data Pre-processing
Data pre-processing is an often neglected but important step in the data mining process. The phrase "Garbage In, Garbage Out" is particularly applicable to data mining and machine learning projects...

, model
Statistical model
A statistical model is a formalization of relationships between variables in the form of mathematical equations. A statistical model describes how one or more random variables are related to one or more random variables. The model is statistical as the variables are not deterministically but...

 and inference
Statistical inference
In statistics, statistical inference is the process of drawing conclusions from data that are subject to random variation, for example, observational errors or sampling variation...

 considerations, interestingness metrics, complexity
Computational complexity theory
Computational complexity theory is a branch of the theory of computation in theoretical computer science and mathematics that focuses on classifying computational problems according to their inherent difficulty, and relating those classes to each other...

 considerations, post-processing of found structure, visualization
Data visualization
Data visualization is the study of the visual representation of data, meaning "information that has been abstracted in some schematic form, including attributes or variables for the units of information"....

 and online updating
Online algorithm
In computer science, an online algorithm is one that can process its input piece-by-piece in a serial fashion, i.e., in the order that the input is fed to the algorithm, without having the entire input available from the start. In contrast, an offline algorithm is given the whole problem data from...

.

The term is a buzzword
Buzzword
A buzzword is a term of art, salesmanship, politics, or technical jargon that is used in the media and wider society outside of its originally narrow technical context....

, and is frequently misused to mean any form of large scale data or information processing (collection, extraction
Information extraction
Information extraction is a type of information retrieval whose goal is to automatically extract structured information from unstructured and/or semi-structured machine-readable documents. In most of the cases this activity concerns processing human language texts by means of natural language...

, warehousing
Data warehouse
In computing, a data warehouse is a database used for reporting and analysis. The data stored in the warehouse is uploaded from the operational systems. The data may pass through an operational data store for additional operations before it is used in the DW for reporting.A data warehouse...

, analysis
Data analysis
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making...

 and statistics) but also generalized to any kind of computer decision support system
Decision support system
A decision support system is a computer-based information system that supports business or organizational decision-making activities. DSSs serve the management, operations, and planning levels of an organization and help to make decisions, which may be rapidly changing and not easily specified in...

 including artificial intelligence
Artificial intelligence
Artificial intelligence is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its...

, machine learning
Machine learning
Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...

 and business intelligence
Business intelligence
Business intelligence mainly refers to computer-based techniques used in identifying, extracting, and analyzing business data, such as sales revenue by products and/or departments, or by associated costs and incomes....

. In the proper use of the word, the key term is discovery
Discovery (observation)
Discovery is the act of detecting something new, or something "old" that had been unknown. With reference to science and academic disciplines, discovery is the observation of new phenomena, new actions, or new events and providing new reasoning to explain the knowledge gathered through such...

, commonly defined as "detecting something new". Even the popular book "Data mining: Practical machine learning tools
and techniques with Java" (which covers mostly machine learning
Machine learning
Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...

 material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis
Data analysis
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making...

" or "analytics
Analytics
Analytics is the application of computer technology, operational research, and statistics to solve problems in business and industry. Analytics is carried out within an information system: while, in the past, statistics and mathematics could be studied without computers and software, analytics has...

" or when referring to actual methods, artificial intelligence
Artificial intelligence
Artificial intelligence is the intelligence of machines and the branch of computer science that aims to create it. AI textbooks define the field as "the study and design of intelligent agents" where an intelligent agent is a system that perceives its environment and takes actions that maximize its...

 and machine learning
Machine learning
Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...

 are more appropriate.

The actual data-mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection
Anomaly detection
Anomaly detection, also referred to as outlier detection refers to detecting patterns in a given data set that do not conform to an established normal behavior....

) and dependencies (association rule mining). This usually involves using database techniques such as spatial indexes. These patterns can then be seen as a kind of summary of the input data, and used in further analysis or for example in machine learning
Machine learning
Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...

 and predictive analytics
Predictive analytics
Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future events....

. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system
Decision support system
A decision support system is a computer-based information system that supports business or organizational decision-making activities. DSSs serve the management, operations, and planning levels of an organization and help to make decisions, which may be rapidly changing and not easily specified in...

. Neither the data collection, data preparation nor result interpretation and reporting are part of the data mining step, but do belong to the overall KDD process as additional steps.

The related terms data dredging
Data dredging
Data dredging is the inappropriate use of data mining to uncover misleading relationships in data. Data-snooping bias is a form of statistical bias that arises from this misuse of statistics...

, data fishing and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.

Background

The manual extraction of patterns from data
Data
The term data refers to qualitative or quantitative attributes of a variable or set of variables. Data are typically the results of measurements and can be the basis of graphs, images, or observations of a set of variables. Data are often viewed as the lowest level of abstraction from which...

 has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem
Bayes' theorem
In probability theory and applications, Bayes' theorem relates the conditional probabilities P and P. It is commonly used in science and engineering. The theorem is named for Thomas Bayes ....

 (1700s) and regression analysis
Regression analysis
In statistics, regression analysis includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables...

 (1800s). The proliferation, ubiquity and increasing power of computer technology has increased data collection, storage and manipulations. As data set
Data set
A data set is a collection of data, usually presented in tabular form. Each column represents a particular variable. Each row corresponds to a given member of the data set in question. Its values for each of the variables, such as height and weight of an object or values of random numbers. Each...

s have grown in size and complexity, direct hands-on data analysis has increasingly been augmented with indirect, automatic data processing. This has been aided by other discoveries in computer science, such as neural networks
Neural Networks
Neural Networks is the official journal of the three oldest societies dedicated to research in neural networks: International Neural Network Society, European Neural Network Society and Japanese Neural Network Society, published by Elsevier...

, cluster analysis, genetic algorithms (1950s), decision trees
Decision tree learning
Decision tree learning, used in statistics, data mining and machine learning, uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value. More descriptive names for such tree models are classification trees or regression trees...

 (1960s) and support vector machines (1990s). Data mining is the process of applying these methods to data with the intention of uncovering hidden patterns in large data sets. It bridges the gap from applied statistics and artificial intelligence (which usually provide the mathematical background) to database management by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to larger data sets.

Research and evolution

The premier professional body in the field is the Association for Computing Machinery
Association for Computing Machinery
The Association for Computing Machinery is a learned society for computing. It was founded in 1947 as the world's first scientific and educational computing society. Its membership is more than 92,000 as of 2009...

's Special Interest Group on knowledge discovery
Knowledge discovery
Knowledge discovery is a concept of the field of computer science that describes the process of automatically searching large volumes of data for patterns that can be considered knowledge about the data . It is often described as deriving knowledge from the input data...

 and Data Mining (SIGKDD
SIGKDD
SIGKDD is the Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining. It became an official ACM SIG in 1998.- Conferences :...

). Since 1989 they have hosted an annual international conference and published its proceedings, and since 1999 have published a biannual academic journal
Academic journal
An academic journal is a peer-reviewed periodical in which scholarship relating to a particular academic discipline is published. Academic journals serve as forums for the introduction and presentation for scrutiny of new research, and the critique of existing research...

 titled "SIGKDD Explorations".

Computer science conferences on data mining include:
  • CIKM – ACM Conference on Information and Knowledge Management
    Conference on Information and Knowledge Management
    The ACM Conference on Information and Knowledge Management is an annual computer science research conference dedicated to information and knowledge management. Since the first event in 1992, the conference has evolved into one of the major forums for research on database management, information...

  • DMIN – International Conference on Data Mining
  • DMKD – Research Issues on Data Mining and Knowledge Discovery
  • ECDM – European Conference on Data Mining
  • ECML-PKDD – European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases
  • EDM – International Conference on Educational Data Mining
  • ICDM – IEEE International Conference on Data Mining
  • KDD – ACM SIGKDD Conference on Knowledge Discovery and Data Mining
  • MLDM – Machine Learning and Data Mining in Pattern Recognition
  • PAKDD – The annual Pacific-Asia Conference on Knowledge Discovery and Data Mining
  • PAW – Predictive Analytics World
  • SDM – SIAM International Conference on Data Mining (SIAM
    Society for Industrial and Applied Mathematics
    The Society for Industrial and Applied Mathematics was founded by a small group of mathematicians from academia and industry who met in Philadelphia in 1951 to start an organization whose members would meet periodically to exchange ideas about the uses of mathematics in industry. This meeting led...

    )
  • SSTD – Symposium on Spatial and Temporal Databases


Data mining topics are present on most data management / database conferences.

Process

The knowledge discovery in databases (KDD) process is commonly defined with the stages
(1) Selection (2) Preprocessing (3) Transformation (4) Data Mining (5) Interpretation/Evaluation.
It exists however in many variations of this theme such as the CRoss Industry Standard Process for Data Mining (CRISP-DM)
CRISP-DM
CRISP-DM stands for Cross Industry Standard Process for Data Mining. It is a data mining process model that describes commonly used approaches that expert data miners use to tackle problems. Polls conducted in 2002, 2004, and 2007 show that it is the leading methodology used by data miners...

 which defines six phases: (1) Business Understanding, (2) Data Understanding, (3) Data Preparation, (4) Modeling, (5) Evaluation, and (6) Deployment
or a simplified process such as (1) Pre-processing, (2) Data mining, and (3) Results validation.

Pre-processing

Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target dataset must be large enough to contain these patterns while remaining concise enough to be mined in an acceptable timeframe. A common source for data is a data mart
Data mart
A data mart is the access layer of the data warehouse environment that is used to get data out to the users. The data mart is a subset of the data warehouse which is usually oriented to a specific business line or team.- Terminology :...

 or data warehouse
Data warehouse
In computing, a data warehouse is a database used for reporting and analysis. The data stored in the warehouse is uploaded from the operational systems. The data may pass through an operational data store for additional operations before it is used in the DW for reporting.A data warehouse...

. Pre-process is essential to analyze the multivariate
Multivariate statistics
Multivariate statistics is a form of statistics encompassing the simultaneous observation and analysis of more than one statistical variable. The application of multivariate statistics is multivariate analysis...

 datasets before data mining.

The target set is then cleaned. Data cleaning removes the observations with noise
Statistical noise
Statistical noise is the colloquialism for recognized amounts of unexplained variation in a sample. See errors and residuals in statistics....

 and missing data.

Data mining

Data mining involves six common classes of tasks:
  • Anomaly detection
    Anomaly detection
    Anomaly detection, also referred to as outlier detection refers to detecting patterns in a given data set that do not conform to an established normal behavior....

     (Outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors and require further investigation.
  • Association rule learning
    Association rule learning
    In data mining, association rule learning is a popular andwell researched method for discovering interesting relations between variablesin large databases. Piatetsky-Shapirodescribes analyzing and presenting...

     (Dependency modeling) – Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.
  • Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.
  • Classification – is the task of generalizing known structure to apply to new data. For example, an email program might attempt to classify an email as legitimate or spam.
  • Regression
    Regression analysis
    In statistics, regression analysis includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables...

     – Attempts to find a function which models the data with the least error.
  • Summarization – providing a more compact representation of the data set, including visualization and report generation.

Results validation

The final step of knowledge discovery from data is to verify the patterns produced by the data mining algorithms occur in the wider data set. Not all patterns found by the data mining algorithms are necessarily valid. It is common for the data mining algorithms to find patterns in the training set which are not present in the general data set. This is called overfitting
Overfitting
In statistics, overfitting occurs when a statistical model describes random error or noise instead of the underlying relationship. Overfitting generally occurs when a model is excessively complex, such as having too many parameters relative to the number of observations...

. To overcome this, the evaluation uses a test set
Test set
A test set is a set of data used in various areas of information science to assess the strength and utility of a predictive relationship. Test sets are used in artificial intelligence, machine learning, genetic programming, intelligent systems, and statistics...

 of data on which the data mining algorithm was not trained. The learned patterns are applied to this test set and the resulting output is compared to the desired output. For example, a data mining algorithm trying to distinguish spam from legitimate emails would be trained on a training set
Training set
A training set is a set of data used in various areas of information science to discover potentially predictive relationships. Training sets are used in artificial intelligence, machine learning, genetic programming, intelligent systems, and statistics...

 of sample emails. Once trained, the learned patterns would be applied to the test set of emails on which it had not been trained. The accuracy of these patterns can then be measured from how many emails they correctly classify. A number of statistical methods may be used to evaluate the algorithm such as ROC curves
Receiver operating characteristic
In signal detection theory, a receiver operating characteristic , or simply ROC curve, is a graphical plot of the sensitivity, or true positive rate, vs. false positive rate , for a binary classifier system as its discrimination threshold is varied...

.

If the learned patterns do not meet the desired standards, then it is necessary to reevaluate and change the pre-processing and data mining. If the learned patterns do meet the desired standards then the final step is to interpret the learned patterns and turn them into knowledge.

Standards

There have been some efforts to define standards for the data mining process, for example the 1999 European Cross Industry Standard Process for Data Mining (CRISP-DM 1.0) and the 2004 Java Data Mining
Java Data Mining
Java Data Mining is a standard Java API for developing data mining applications and tools. JDM defines an object model and Java API for data mining objects and processes. JDM enables applications to integrate data mining technology for developing predictive analytics applications and tools. The...

 standard (JDM 1.0). Development on successors of these processes (CRISP-DM 2.0 and JDM 2.0) was active in 2006, but has stalled since. JDM 2.0 was withdrawn without reaching a final draft.

For exchanging the extracted models – in particular for the use in predictive analytics
Predictive analytics
Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, data mining and game theory that analyze current and historical facts to make predictions about future events....

 – the key standard is the Predictive Model Markup Language
Predictive Model Markup Language
The Predictive Model Markup Language is an XML-based markup language developed by the Data Mining Group to provide a way for applications to define models related to predictive analytics and data mining and to share those models between PMML-compliant applications.PMML provides applications a...

 (PMML), which is an XML
XML
Extensible Markup Language is a set of rules for encoding documents in machine-readable form. It is defined in the XML 1.0 Specification produced by the W3C, and several other related specifications, all gratis open standards....

-based language developed by the Data Mining Group (DMG) and supported as exchange format by many data mining applications. As the name suggests it only covers prediction models, a particular data mining task of high importance to business applications, however extensions to for example cover subspace clustering have been proposed independently of the DMG.

Notable uses

Games

Since the early 1960s, with the availability of oracles
Oracle machine
In complexity theory and computability theory, an oracle machine is an abstract machine used to study decision problems. It can be visualized as a Turing machine with a black box, called an oracle, which is able to decide certain decision problems in a single operation. The problem can be of any...

 for certain combinatorial games, also called tablebases (e.g. for 3x3-chess) with any beginning configuration, small-board dots-and-boxes, small-board-hex, and certain endgames in chess, dots-and-boxes, and hex; a new area for data mining has been opened. This is the extraction of human-usable strategies from these oracles. Current pattern recognition approaches do not seem to fully acquire the high level of abstraction required to be applied successfully. Instead, extensive experimentation with the tablebases, combined with an intensive study of tablebase-answers to well designed problems and with knowledge of prior art, i.e. pre-tablebase knowledge, is used to yield insightful patterns. Berlekamp in dots-and-boxes etc. and John Nunn
John Nunn
John Denis Martin Nunn is one of England's strongest chess players and once belonged to the world's top ten. He is also a three times world champion in chess problem solving, a chess writer and publisher, and a mathematician....

 in chess
Chess
Chess is a two-player board game played on a chessboard, a square-checkered board with 64 squares arranged in an eight-by-eight grid. It is one of the world's most popular games, played by millions of people worldwide at home, in clubs, online, by correspondence, and in tournaments.Each player...

 endgames are notable examples of researchers doing this work, though they were not and are not involved in tablebase generation.

Business

Data mining in customer relationship management
Customer relationship management
Customer relationship management is a widely implemented strategy for managing a company’s interactions with customers, clients and sales prospects. It involves using technology to organize, automate, and synchronize business processes—principally sales activities, but also those for marketing,...

 applications can contribute significantly to the bottom line. Rather than randomly contacting a prospect or customer through a call center or sending mail, a company can concentrate its efforts on prospects that are predicted to have a high likelihood of responding to an offer. More sophisticated methods may be used to optimize resources across campaigns so that one may predict to which channel and to which offer an individual is most likely to respond—across all potential offers. Additionally, sophisticated applications could be used to automate the mailing. Once the results from data mining (potential prospect/customer and channel/offer) are determined, this "sophisticated application" can either automatically send an e-mail or regular mail. Finally, in cases where many people will take an action without an offer, uplift modeling can be used to determine which people will have the greatest increase in responding if given an offer. Data clustering
Data clustering
Cluster analysis or clustering is the task of assigning a set of objects into groups so that the objects in the same cluster are more similar to each other than to those in other clusters....

 can also be used to automatically discover the segments or groups within a customer data set.

Businesses employing data mining may see a return on investment, but also they recognize that the number of predictive models can quickly become very large. Rather than one model to predict how many customers will churn
Churn rate
Churn rate , in its broadest sense, is a measure of the number of individuals or items moving into or out of a collective over a specific period of time...

, a business could build a separate model for each region and customer type. Then instead of sending an offer to all people that are likely to churn, it may only want to send offers to loyal customers. Finally, it may want to determine which customers are going to be profitable over a window of time and only send the offers to those that are likely to be profitable. In order to maintain this quantity of models, they need to manage model versions and move to automated data mining.

Data mining can also be helpful to human-resources departments in identifying the characteristics of their most successful employees. Information obtained, such as universities attended by highly successful employees, can help HR focus recruiting efforts accordingly. Additionally, Strategic Enterprise Management applications help a company translate corporate-level goals, such as profit and margin share targets, into operational decisions, such as production plans and workforce levels.

Another example of data mining, often called the market basket analysis, relates to its use in retail sales. If a clothing store records the purchases of customers, a data-mining system could identify those customers who favor silk shirts over cotton ones. Although some explanations of relationships may be difficult, taking advantage of it is easier. The example deals with association rules within transaction-based data. Not all data are transaction based and logical or inexact rules
Rule of inference
In logic, a rule of inference, inference rule, or transformation rule is the act of drawing a conclusion based on the form of premises interpreted as a function which takes premises, analyses their syntax, and returns a conclusion...

 may also be present within a database
Database
A database is an organized collection of data for one or more purposes, usually in digital form. The data are typically organized to model relevant aspects of reality , in a way that supports processes requiring this information...

.

Market basket analysis has also been used to identify the purchase patterns of the Alpha consumer
Alpha consumer
An Alpha Consumer is someone that plays a key role in connecting with the concept behind a product, then adopting that product, and finally validating it for the rest of society...

. Alpha Consumers are people that play a key role in connecting with the concept behind a product, then adopting that product, and finally validating it for the rest of society. Analyzing the data collected on this type of user has allowed companies to predict future buying trends and forecast supply demands.

Data Mining is a highly effective tool in the catalog marketing industry. Catalogers have a rich history of customer transactions on millions of customers dating back several years. Data mining tools can identify patterns among customers and help identify the most likely customers to respond to upcoming mailing campaigns.

Data Mining for business applications is a component which needs to be integrated into a complex modelling and decision making process.
Reactive Business Intelligence (RBI)
Reactive Business Intelligence
Reactive Business Intelligence advocates an holistic approach that integrates data mining, modeling and interactive visualization, into an end-to-end discovery and continuous innovation process powered by human and automated learning....


advocates a holistic approach that integrates data mining, modeling
Modeling and simulation
Modeling and simulation is the use of models, including emulators, prototypes, simulators, and stimulators, either statically or over time, to develop data as a basis for making managerial or technical decisions. The terms "modeling" and "simulation" are often used interchangeably.The use of...

 and interactive visualization
Interactive visualization
Interactive visualization is a branch of graphic visualization in computer science that involves studying how humans interact with computers to create graphic illustrations of information and how this process can be made more efficient....

, into an end-to-end discovery and continuous innovation process powered by human and automated learning.
In the area of decision making
Decision making
Decision making can be regarded as the mental processes resulting in the selection of a course of action among several alternative scenarios. Every decision making process produces a final choice. The output can be an action or an opinion of choice.- Overview :Human performance in decision terms...

 the RBI
Reactive Business Intelligence
Reactive Business Intelligence advocates an holistic approach that integrates data mining, modeling and interactive visualization, into an end-to-end discovery and continuous innovation process powered by human and automated learning....

 approach has been used to mine the knowledge which is progressively acquired from the decision maker and
self-tune the decision method accordingly.

Related to an integrated-circuit production line, an example of data mining is described in the paper "Mining IC Test Data to Optimize VLSI Testing." In this paper the application of data mining and decision analysis to the problem of die-level functional test is described. Experiments mentioned in this paper demonstrate the ability of applying a system of mining historical die-test data to create a probabilistic model of patterns of die failure. These patterns are then utilized to decide in real time which die to test next and when to stop testing. This system has been shown, based on experiments with historical test data, to have the potential to improve profits on mature IC products.

Science and engineering

In recent years, data mining has been used widely in the areas of science and engineering, such as bioinformatics, genetics
Genetics
Genetics , a discipline of biology, is the science of genes, heredity, and variation in living organisms....

, medicine
Medicine
Medicine is the science and art of healing. It encompasses a variety of health care practices evolved to maintain and restore health by the prevention and treatment of illness....

, education
Educational data mining
Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in. A key area of EDM is mining computer logs of...

 and electrical power engineering.

In the study of human genetics, an important goal is to understand the mapping relationship between the inter-individual variation in human DNA
DNA
Deoxyribonucleic acid is a nucleic acid that contains the genetic instructions used in the development and functioning of all known living organisms . The DNA segments that carry this genetic information are called genes, but other DNA sequences have structural purposes, or are involved in...

 sequences and variability in disease susceptibility. In lay terms, it is to find out how the changes in an individual's DNA sequence affect the risk of developing common diseases such as cancer
Cancer
Cancer , known medically as a malignant neoplasm, is a large group of different diseases, all involving unregulated cell growth. In cancer, cells divide and grow uncontrollably, forming malignant tumors, and invade nearby parts of the body. The cancer may also spread to more distant parts of the...

. This is very important to help improve the diagnosis, prevention and treatment of the diseases. The data mining method that is used to perform this task is known as multifactor dimensionality reduction
Multifactor dimensionality reduction
Multifactor dimensionality reduction is a data mining approach for detecting and characterizing combinations of attributes or independent variables that interact to influence a dependent or class variable...

.

In the area of electrical power engineering, data mining methods have been widely used for condition monitoring
Condition monitoring
Condition monitoring is the process of monitoring a parameter of condition in machinery, such that a significant change is indicative of a developing failure. It is a major component of predictive maintenance. The use of conditional monitoring allows maintenance to be scheduled, or other actions...

 of high voltage electrical equipment. The purpose of condition monitoring is to obtain valuable information on the insulation's health status of the equipment. Data clustering
Data clustering
Cluster analysis or clustering is the task of assigning a set of objects into groups so that the objects in the same cluster are more similar to each other than to those in other clusters....

 such as self-organizing map
Self-organizing map
A self-organizing map or self-organizing feature map is a type of artificial neural network that is trained using unsupervised learning to produce a low-dimensional , discretized representation of the input space of the training samples, called a map...

 (SOM) has been applied on the vibration monitoring and analysis of transformer on-load tap-changers (OLTCS). Using vibration monitoring, it can be observed that each tap change operation generates a signal that contains information about the condition of the tap changer contacts and the drive mechanisms. Obviously, different tap positions will generate different signals. However, there was considerable variability amongst normal condition signals for exactly the same tap position. SOM has been applied to detect abnormal conditions and to estimate the nature of the abnormalities.

Data mining methods have also been applied for dissolved gas analysis
Dissolved gas analysis
Dissolved gas analysis is the study of dissolved gases in insulating fluid such as transformer oil. Insulating materials within transformers and electrical equipment break down to liberate gases within the unit. The distribution of these gases can be related to the type of electrical fault, and...

 (DGA) on power transformers. DGA, as a diagnostics for power transformer, has been available for many years. Methods such as SOM has been applied to analyze data and to determine trends which are not obvious to the standard DGA ratio methods such as Duval Triangle.

A fourth area of application for data mining in science/engineering is within educational research, where data mining has been used to study the factors leading students to choose to engage in behaviors which reduce their learning and to understand the factors influencing university student retention. A similar example of the social application of data mining is its use in expertise finding systems
Expertise finding
The Oxford English Dictionary defines "expertise" as follows: a. Expert opinion or knowledge, often obtained through the action of submitting a matter to, and its consideration by, experts; an expert's appraisal, valuation, or report. b. The quality or state of being expert; skill or expertness in...

, whereby descriptors of human expertise are extracted, normalized and classified so as to facilitate the finding of experts, particularly in scientific and technical fields. In this way, data mining can facilitate Institutional memory
Institutional memory
Institutional memory is a collective set of facts, concepts, experiences and know-how held by a group of people. As it transcends the individual, it requires the ongoing transmission of these memories between members of this group...

.

Other examples of applying data mining method applications are biomedical data facilitated by domain ontologies, mining clinical trial data, traffic analysis
Traffic analysis
Traffic analysis is the process of intercepting and examining messages in order to deduce information from patterns in communication. It can be performed even when the messages are encrypted and cannot be decrypted. In general, the greater the number of messages observed, or even intercepted and...

 using SOM, et cetera.

In adverse drug reaction surveillance, the Uppsala Monitoring Centre
Uppsala Monitoring Centre
The Uppsala Monitoring Centre , located in Uppsala, Sweden, is the field name for the World Health Organization Collaborating Centre for International Drug Monitoring...

 has, since 1998, used data mining methods to routinely screen for reporting patterns indicative of emerging drug safety issues in the WHO global database of 4.6 million suspected adverse drug reaction
Adverse drug reaction
An adverse drug reaction is an expression that describes harm associated with the use of given medications at a normal dosage. ADRs may occur following a single dose or prolonged administration of a drug or result from the combination of two or more drugs...

 incidents. Recently, similar methodology has been developed to mine large collections of electronic health records for temporal patterns associating drug prescriptions to medical diagnoses.

Spatial data mining

Spatial data mining is the application of data mining methods to spatial data. Spatial data mining follows along the same functions in data mining, with the end objective to find patterns in geography. So far, data mining and Geographic Information Systems (GIS) have existed as two separate technologies, each with its own methods, traditions and approaches to visualization and data analysis. Particularly, most contemporary GIS have only very basic spatial analysis functionality. The immense explosion in geographically referenced data occasioned by developments in IT, digital mapping, remote sensing, and the global diffusion of GIS emphasizes the importance of developing data driven inductive approaches to geographical analysis and modeling.

Data mining, which is the partially automated search for hidden patterns in large databases, offers great potential benefits for applied GIS-based decision-making. Recently, the task of integrating these two technologies has become critical, especially as various public and private sector organizations possessing huge databases with thematic and geographically referenced data begin to realize the huge potential of the information hidden there. Among those organizations are:
  • offices requiring analysis or dissemination of geo-referenced statistical data
  • public health services searching for explanations of disease clusters
  • environmental agencies assessing the impact of changing land-use patterns on climate change
  • geo-marketing companies doing customer segmentation based on spatial location.

Challenges

Geospatial data repositories tend to be very large. Moreover, existing GIS datasets are often splintered into feature and attribute components, that are conventionally archived in hybrid data management systems. Algorithmic requirements differ substantially for relational (attribute) data management and for topological (feature) data management. Related to this is the range and diversity of geographic data formats, that also presents unique challenges. The digital geographic data revolution is creating new types of data formats beyond the traditional "vector" and "raster" formats. Geographic data repositories increasingly include ill-structured data such as imagery and geo-referenced multi-media.

There are several critical research challenges in geographic knowledge discovery and data mining. Miller and Han offer the following list of emerging research topics in the field:
  • Developing and supporting geographic data warehouses – Spatial properties are often reduced to simple aspatial attributes in mainstream data warehouses. Creating an integrated GDW requires solving issues in spatial and temporal data interoperability, including differences in semantics, referencing systems, geometry, accuracy and position.
  • Better spatio-temporal representations in geographic knowledge discovery – Current geographic knowledge discovery (GKD) methods generally use very simple representations of geographic objects and spatial relationships. Geographic data mining methods should recognize more complex geographic objects (lines and polygons) and relationships (non-Euclidean distances, direction, connectivity and interaction through attributed geographic space such as terrain). Time needs to be more fully integrated into these geographic representations and relationships.
  • Geographic knowledge discovery using diverse data types – GKD methods should be developed that can handle diverse data types beyond the traditional raster and vector models, including imagery and geo-referenced multimedia, as well as dynamic data types (video streams, animation).


In four annual surveys of data miners
Rexer's Annual Data Miner Survey
Rexer Analytics’s Annual Data Miner Survey is the largest survey of data mining professionals in the industry. It consists of approximately 50 multiple choice and open-ended questions that cover seven general areas of data mining science and practice: Field and goals, Algorithms, Models, Tools...

, data mining practitioners consistently identified that they faced three key challenges more than any others:
  • Dirty Data
  • Explaining Data Mining to Others
  • Unavailability of Data / Difficult Access to Data

In the 2010 survey
Rexer's Annual Data Miner Survey
Rexer Analytics’s Annual Data Miner Survey is the largest survey of data mining professionals in the industry. It consists of approximately 50 multiple choice and open-ended questions that cover seven general areas of data mining science and practice: Field and goals, Algorithms, Models, Tools...

 data miners also shared their experiences in overcoming these challenges.

Visual Data Mining

The process of turning from analogical into digital, large data sets have been generated, collected and stored discovering statistical patterns, trends and information which is hidden in data, in order to build predictive patterns. A study found that Visual Data Mining is faster and much more intuitive than traditional data mining.

Surveillance

Prior data mining to stop terrorist programs under the U.S. government include the Total Information Awareness (TIA) program, Secure Flight (formerly known as Computer-Assisted Passenger Prescreening System (CAPPS II)), Analysis, Dissemination, Visualization, Insight, Semantic Enhancement (ADVISE
ADVISE
ADVISE is a research and development program within the United States Department of Homeland Security Threat and Vulnerability Testing and Assessment portfolio...

), and the Multi-state Anti-Terrorism Information Exchange (MATRIX
Matrix
- Science and mathematics :* Matrix , a mathematical object generally represented as an array of numbers** Matrix calculus, a notation for calculus operations on matrix spaces** Identity matrix...

). These programs have been discontinued due to controversy over whether they violate the US Constitution's 4th amendment, although many programs that were formed under them continue to be funded by different organizations, or under different names.

Two plausible data mining methods in the context of combating terrorism include "pattern mining" and "subject-based data mining".

Pattern mining


"Pattern mining" is a data mining method that involves finding existing pattern
Pattern
A pattern, from the French patron, is a type of theme of recurring events or objects, sometimes referred to as elements of a set of objects.These elements repeat in a predictable manner...

s in data. In this context patterns often means association rules. The original motivation for searching association rules came from the desire to analyze supermarket transaction data, that is, to examine customer behavior in terms of the purchased products. For example, an association rule "beer ⇒ potato chips (80%)" states that four out of five customers that bought beer also bought potato chips.

In the context of pattern mining as a tool to identify terrorist activity, the National Research Council
United States National Research Council
The National Research Council of the USA is the working arm of the United States National Academies, carrying out most of the studies done in their names.The National Academies include:* National Academy of Sciences...

 provides the following definition: "Pattern-based data mining looks for patterns (including anomalous data patterns) that might be associated with terrorist activity — these patterns might be regarded as small signals in a large ocean of noise." Pattern Mining includes new areas such a Music Information Retrieval
Music information retrieval
Music information retrieval is the interdisciplinary science of retrieving information from music. MIR is a small but growing field of research with many real-world applications...

 (MIR) where patterns seen both in the temporal and non temporal domains are imported to classical knowledge discovery search methods.

Subject-based data mining


"Subject-based data mining" is a data mining method involving the search for associations between individuals in data. In the context of combating terrorism, the National Research Council
United States National Research Council
The National Research Council of the USA is the working arm of the United States National Academies, carrying out most of the studies done in their names.The National Academies include:* National Academy of Sciences...

 provides the following definition: "Subject-based data mining uses an initiating individual or other datum that is considered, based on other information, to be of high interest, and the goal is to determine what other persons or financial transactions or movements, etc., are related to that initiating datum."

Knowledge grid

Researchers at the University of Calabria
University of Calabria
The University of Calabria is a state-run university in Italy.Located in Arcavacata di Rende, a suburb of Cosenza, the university was founded in 1972...

 developed a Knowledge Grid architecture for distributed knowledge discovery, based on grid computing
Grid computing
Grid computing is a term referring to the combination of computer resources from multiple administrative domains to reach a common goal. The grid can be thought of as a distributed system with non-interactive workloads that involve a large number of files...

.

Privacy concerns and ethics

Some people believe that data mining itself is ethically neutral. It is important to note that the term data mining has no ethical implications. The term is often associated with the mining of information in relation to peoples' behavior. However, data mining is a statistical method that is applied to a set of information, or a data set. Associating these data sets with people is an extreme narrowing of the types of data that are available in today's technological society. Examples could range from a set of crash test data for passenger vehicles, to the performance of a group of stocks. These types of data sets make up a great proportion of the information available to be acted on by data mining methods, and rarely have ethical concerns associated with them. However, the ways in which data mining can be used can raise questions regarding privacy, legality, and ethics. In particular, data mining government or commercial data sets for national security or law enforcement purposes, such as in the Total Information Awareness Program or in ADVISE
ADVISE
ADVISE is a research and development program within the United States Department of Homeland Security Threat and Vulnerability Testing and Assessment portfolio...

, has raised privacy concerns.

Data mining requires data preparation which can uncover information or patterns which may compromise confidentiality and privacy obligations. A common way for this to occur is through data aggregation
Aggregate function
In computer science, an aggregate function is a function where the values of multiple rows are grouped together as input on certain criteria to form a single value of more significant meaning or measurement such as a set, a bag or a list....

. Data aggregation is when the data are accrued, possibly from various sources, and put together so that they can be analyzed. This is not data mining per se, but a result of the preparation of data before and for the purposes of the analysis. The threat to an individual's privacy comes into play when the data, once compiled, cause the data miner, or anyone who has access to the newly compiled data set, to be able to identify specific individuals, especially when originally the data were anonymous.

It is recommended that an individual is made aware of the following before data are collected:
  • the purpose of the data collection and any data mining projects,
  • how the data will be used,
  • who will be able to mine the data and use them,
  • the security surrounding access to the data, and in addition,
  • how collected data can be updated.


In the United States
United States
The United States of America is a federal constitutional republic comprising fifty states and a federal district...

, privacy concerns have been somewhat addressed by their congress via the passage of regulatory controls such as the Health Insurance Portability and Accountability Act
Health Insurance Portability and Accountability Act
The Health Insurance Portability and Accountability Act of 1996 was enacted by the U.S. Congress and signed by President Bill Clinton in 1996. It was originally sponsored by Sen. Edward Kennedy and Sen. Nancy Kassebaum . Title I of HIPAA protects health insurance coverage for workers and their...

 (HIPAA). The HIPAA requires individuals to be given "informed consent" regarding any information that they provide and its intended future uses by the facility receiving that information. According to an article in Biotech Business Week, "In practice, HIPAA may not offer any greater protection than the longstanding regulations in the research arena, says the AAHC. More importantly, the rule's goal of protection through informed consent is undermined by the complexity of consent forms that are required of patients and participants, which approach a level of incomprehensibility to average individuals." This underscores the necessity for data anonymity in data aggregation practices.

One may additionally modify the data so that they are anonymous, so that individuals may not be readily identified. However, even de-identified data sets can contain enough information to identify individuals, as occurred when journalists were able to find several individuals based on a set of search histories that were inadvertently released by AOL.

Free libre open-source data-mining software and applications

  1. Carrot2
    Carrot2
    Carrot² is an open source search results clustering engine. It can automatically cluster small collections of documents, e.g. search results or document abstracts, into thematic categories. Apart from two specialized search results clustering algorithms, Carrot² offers ready-to-use components for...

     – Text and search results clustering framework.
  2. Chemicalize.org
    Chemicalize.org
    chemicalize.org is a free chemical structure miner and web search engine developed and owned by ChemAxon. The main purpose of chemicalize.org is to identify chemical names on websites and convert them to chemical structures...

     – A chemical structure miner and web search engine.
  3. ELKI
    Environment for DeveLoping KDD-Applications Supported by Index-Structures
    ELKI is a knowledge discovery in databases software framework developed for use in research and teaching by the database systems research unit of Professor Hans-Peter Kriegel at the Ludwig Maximilian University of Munich, Germany...

     – A university research project with advanced cluster analysis and outlier detection
    Anomaly detection
    Anomaly detection, also referred to as outlier detection refers to detecting patterns in a given data set that do not conform to an established normal behavior....

     methods written in Java
    Java (programming language)
    Java is a programming language originally developed by James Gosling at Sun Microsystems and released in 1995 as a core component of Sun Microsystems' Java platform. The language derives much of its syntax from C and C++ but has a simpler object model and fewer low-level facilities...

     language.
  4. GATE
    General Architecture for Text Engineering
    General Architecture for Text Engineering or GATE is a Java suite of tools originally developed at the University of Sheffield beginning in 1995 and now used worldwide by a wide community of scientists, companies, teachers and students for all sorts of natural language processing tasks, including...

     – Natural language processing
    Natural language processing
    Natural language processing is a field of computer science and linguistics concerned with the interactions between computers and human languages; it began as a branch of artificial intelligence....

     and language engineering tool.
  5. JHepWork
    JHepWork
    jHepWork is an interactive framework for scientific computation, data analysis and data visualization designed for scientists, engineers and students...

     – Java cross-platform
    Cross-platform
    In computing, cross-platform, or multi-platform, is an attribute conferred to computer software or computing methods and concepts that are implemented and inter-operate on multiple computer platforms...

     data analysis framework developed at ANL
    Argonne National Laboratory
    Argonne National Laboratory is the first science and engineering research national laboratory in the United States, receiving this designation on July 1, 1946. It is the largest national laboratory by size and scope in the Midwest...

    .
  6. KNIME
    KNIME
    KNIME, the Konstanz Information Miner, is a user friendly, coherent open source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept...

     – The Konstanz Information Miner, a user friendly and comprehensive data analytics framework.
  7. NLTK or Natural Language Toolkit
    Natural Language Toolkit
    Natural Language Toolkit or, more commonly, NLTK is a suite of libraries and programs for symbolic and statistical natural language processing for the Python programming language. NLTK includes graphical demonstrations and sample data...

     – A suite of libraries and programs for symbolic and statistical natural language processing (NLP) for the Python
    Python (programming language)
    Python is a general-purpose, high-level programming language whose design philosophy emphasizes code readability. Python claims to "[combine] remarkable power with very clear syntax", and its standard library is large and comprehensive...

     language.
  8. Orange
    Orange (software)
    Orange is a component-based data mining and machine learning software suite, featuring friendly yet powerful and flexible visual programming front-end for explorative data analysis and visualization, and Python bindings and libraries for scripting...

     – A component-based data mining and machine learning
    Machine learning
    Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...

     software suite written in Python
    Python
    The Pythonidae, commonly known simply as pythons, from the Greek word python-πυθων, are a family of non-venomous snakes found in Africa, Asia and Australia. Among its members are some of the largest snakes in the world...

     language.
  9. R
    R (programming language)
    R is a programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians for developing statistical software, and R is widely used for statistical software development and data analysis....

     – A programming language
    Programming language
    A programming language is an artificial language designed to communicate instructions to a machine, particularly a computer. Programming languages can be used to create programs that control the behavior of a machine and/or to express algorithms precisely....

     and software environment for statistical
    Statistics
    Statistics is the study of the collection, organization, analysis, and interpretation of data. It deals with all aspects of this, including the planning of data collection in terms of the design of surveys and experiments....

     computing, data mining and graphics. It is part of the GNU project
    GNU Project
    The GNU Project is a free software, mass collaboration project, announced on September 27, 1983, by Richard Stallman at MIT. It initiated GNU operating system development in January, 1984...

    .
  10. RapidMiner – An environment for machine learning
    Machine learning
    Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...

     and data mining experiments.
  11. UIMA
    Uima
    UIMA stands for Unstructured Information Management Architecture. An OASIS standard as of March 2009, UIMA is to date the only industry standard for content analytics....

     – The UIMA (Unstructured Information Management Architecture) is a component framework for analyzing unstructured content such as text, audio and video, originally developed by IBM.
  12. Weka
    Weka (machine learning)
    Weka is a popular suite of machine learning software written in Java, developed at the University of Waikato, New Zealand...

     – A suite of machine learning software written in the Java
    Java (programming language)
    Java is a programming language originally developed by James Gosling at Sun Microsystems and released in 1995 as a core component of Sun Microsystems' Java platform. The language derives much of its syntax from C and C++ but has a simpler object model and fewer low-level facilities...

     language.


In 2010, the open source R language overtook other tools to become the tool used by more data miners (43%) than any other.

Commercial data-mining software and applications

  • Microsoft Analysis Services
    Microsoft Analysis Services
    Microsoft SQL Server Analysis Services is part of Microsoft SQL Server, a database management system. Microsoft has included a number of services in SQL Server related to business intelligence and data warehousing. These services include Integration Services and Analysis Services...

     data mining software provided by Microsoft
    Microsoft
    Microsoft Corporation is an American public multinational corporation headquartered in Redmond, Washington, USA that develops, manufactures, licenses, and supports a wide range of products and services predominantly related to computing through its various product divisions...

  • SAS Enterprise Miner – data mining software provided by the SAS Institute
    SAS Institute
    SAS Institute Inc. , headquartered in Cary, North Carolina, USA, has been a major producer of software since it was founded in 1976 by Anthony Barr, James Goodnight, John Sall and Jane Helwig...

    .
  • SPSS Modeler – data mining software provided by IBM
    IBM
    International Business Machines Corporation or IBM is an American multinational technology and consulting corporation headquartered in Armonk, New York, United States. IBM manufactures and sells computer hardware and software, and it offers infrastructure, hosting and consulting services in areas...

     SPSS
    SPSS
    SPSS is a computer program used for survey authoring and deployment , data mining , text analytics, statistical analysis, and collaboration and deployment ....

    .
  • STATISTICA
    STATISTICA
    STATISTICA is a statistics and analytics software package developed by StatSoft. STATISTICA provides data analysis, data management, data mining, and data visualization procedures...

     Data Miner – data mining software provided by StatSoft
    StatSoft
    StatSoft is a global provider of enterprise and desktop software for data analysis, data management, data visualization, data mining , and quality control.-Company History:...

    .


According to Rexer's Annual Data Miner Survey
Rexer's Annual Data Miner Survey
Rexer Analytics’s Annual Data Miner Survey is the largest survey of data mining professionals in the industry. It consists of approximately 50 multiple choice and open-ended questions that cover seven general areas of data mining science and practice: Field and goals, Algorithms, Models, Tools...

 in 2010, IBM SPSS Modeler, STATISTICA
STATISTICA
STATISTICA is a statistics and analytics software package developed by StatSoft. STATISTICA provides data analysis, data management, data mining, and data visualization procedures...

 Data Miner and R
R (programming language)
R is a programming language and software environment for statistical computing and graphics. The R language is widely used among statisticians for developing statistical software, and R is widely used for statistical software development and data analysis....

 received the strongest satisfaction ratings.

Marketplace surveys

Several researchers and organizations have conducted reviews of data mining tools and surveys of data miners. These identify some of the strengths and weaknesses of the software packages. They also provide an overview of the behaviors, preferences and views of data miners. Some of these reports include:
  • Annual Rexer Analytics Data Miner Surveys
    Rexer's Annual Data Miner Survey
    Rexer Analytics’s Annual Data Miner Survey is the largest survey of data mining professionals in the industry. It consists of approximately 50 multiple choice and open-ended questions that cover seven general areas of data mining science and practice: Field and goals, Algorithms, Models, Tools...

    .
  • Forrester Research
    Forrester Research
    Forrester Research is an independent technology and market research company that provides its clients with advice about technology's impact on business and consumers. Forrester Research has five research centers in the US: Cambridge, Massachusetts; New York, New York; San Francisco, California;...

     2010 Predictive Analytics and Data Mining Solutions report.
  • Gartner
    Gartner
    Gartner, Inc. is an information technology research and advisory firm headquartered in Stamford, Connecticut, United States. It was known as GartnerGroup until 2001....

     2008 "Magic Quadrant" report.
  • Haughton et al.'s 2003 Review of Data Mining Software Packages in The American Statistician
    The American Statistician
    The American Statistician, established in 1947, is a magazine published quarterly by the American Statistical Association.- External links :*...

    .
  • Robert A. Nisbet's 2006 Three Part Series of articles "Data Mining Tools: Which One is Best For CRM?"
  • 2011 Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery in

Methods

Application domains

Application examples

Related topics

Data mining is about analyzing data; for information about extracting information out of data, see:

Further reading

  • Cabena, Peter, Pablo Hadjnian, Rolf Stadler, Jaap Verhees and Alessandro Zanasi (1997). Discovering Data Mining: From Concept to Implementation. Prentice Hall
    Prentice Hall
    Prentice Hall is a major educational publisher. It is an imprint of Pearson Education, Inc., based in Upper Saddle River, New Jersey, USA. Prentice Hall publishes print and digital content for the 6-12 and higher-education market. Prentice Hall distributes its technical titles through the Safari...

    , ISBN 0-13-743980-6.
  • Feldman, Ronen and James Sanger. The Text Mining Handbook. Cambridge University Press
    Cambridge University Press
    Cambridge University Press is the publishing business of the University of Cambridge. Granted letters patent by Henry VIII in 1534, it is the world's oldest publishing house, and the second largest university press in the world...

    , ISBN 978-0-521-83657-9.
  • Guo, Yike and Robert Grossman, editors (1999). High Performance Data Mining: Scaling Algorithms, Applications and Systems. Kluwer Academic Publishers.
  • Hastie, Trevor, Robert Tibshirani and Jerome Friedman (2001). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer, ISBN 0-387-95284-5.
  • Liu, Bing (2007). Web Data Mining: Exploring Hyperlinks, Contents and Usage Data. Springer, ISBN 3-540-37881-2.
  • Nisbet, Robert, John Elder, Gary Miner (2009). Handbook of Statistical Analysis & Data Mining Applications. Academic Press
    Academic Press
    Academic Press is an academic book publisher. Originally independent, it was acquired by Harcourt, Brace & World in 1969. Reed Elsevier bought Harcourt in 2000, and Academic Press is now an imprint of Elsevier....

    /Elsevier. ISBN 9780123747655
  • Poncelet, Pascal, Florent Masseglia and Maguelonne Teisseire, editors (October 2007). "Data Mining Patterns: New Methods and Applications", Information Science Reference. ISBN 978-1-59904-162-9.
  • Pang-Ning Tan, Michael Steinbach and Vipin Kumar (2005). Introduction to Data Mining. ISBN 0-321-32136-7
  • Sergios Theodoridis, Konstantinos Koutroumbas (2009). Pattern Recognition, 4th Edition. Academic Press. ISBN 978-1-59749-272-0.
  • Weiss and Indurkhya. Predictive Data Mining. Morgan Kaufmann. (See also Free Weka software
    Weka (machine learning)
    Weka is a popular suite of machine learning software written in Java, developed at the University of Waikato, New Zealand...

    .)
  • Ye, N. (2003). The Handbook of Data Mining. Mahwah, New Jersey: Lawrence Erlbaum.

External links

The source of this article is wikipedia, the free encyclopedia.  The text of this article is licensed under the GFDL.
 
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