Data mining

Data mining is the process of extracting and discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.^[1] Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use.^[1]^[2]^[3]^[4] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.^[5] Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.^[1]

The term "data mining" is a misnomer because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself.^[6] It also is a buzzword^[7] and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence (e.g., machine learning) and business intelligence. Often the more general terms (large scale) data analysis and analytics—or, when referring to actual methods, artificial intelligence and machine learning—are more appropriate.

The actual data mining task is the semi-automatic or 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), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. 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. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, although they do belong to the overall KDD process as additional steps.

The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large volume of data.^[8]

The related terms data dredging, 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.

^ ^a ^b ^c "Data Mining Curriculum". ACM SIGKDD. 2006-04-30. Archived from the original on 2013-10-14. Retrieved 2014-01-27.
^ Clifton, Christopher (2010). "Encyclopædia Britannica: Definition of Data Mining". Archived from the original on 2011-02-05. Retrieved 2010-12-09.
^ Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2009). "The Elements of Statistical Learning: Data Mining, Inference, and Prediction". Archived from the original on 2009-11-10. Retrieved 2012-08-07.
^ Han, Jaiwei; Kamber, Micheline; Pei, Jian (2011). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann. ISBN 978-0-12-381479-1.
^ Cite error: The named reference Fayyad was invoked but never defined (see the help page).
^ Han, Jiawei; Kamber, Micheline (2001). Data mining: concepts and techniques. Morgan Kaufmann. p. 5. ISBN 978-1-55860-489-6. Thus, data mining should have been more appropriately named "knowledge mining from data," which is unfortunately somewhat long
^ OKAIRP 2005 Fall Conference, Arizona State University Archived 2014-02-01 at the Wayback Machine
^ Olson, D. L. (2007). Data mining in business services. Service Business, 1(3), 181–193. doi:10.1007/s11628-006-0014-7

[acm-1] "Data Mining Curriculum". ACM SIGKDD. 2006-04-30. Archived from the original on 2013-10-14. Retrieved 2014-01-27.

[brittanica-2] Clifton, Christopher (2010). "Encyclopædia Britannica: Definition of Data Mining". Archived from the original on 2011-02-05. Retrieved 2010-12-09.

[elements-3] Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2009). "The Elements of Statistical Learning: Data Mining, Inference, and Prediction". Archived from the original on 2009-11-10. Retrieved 2012-08-07.

[4] Han, Jaiwei; Kamber, Micheline; Pei, Jian (2011). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann. ISBN 978-0-12-381479-1.

[Fayyad-5] Cite error: The named reference Fayyad was invoked but never defined (see the help page).

[han-kamber-6] Han, Jiawei; Kamber, Micheline (2001). Data mining: concepts and techniques. Morgan Kaufmann. p. 5. ISBN 978-1-55860-489-6. Thus, data mining should have been more appropriately named "knowledge mining from data," which is unfortunately somewhat long

[7] OKAIRP 2005 Fall Conference, Arizona State University Archived 2014-02-01 at the Wayback Machine

[8] Olson, D. L. (2007). Data mining in business services. Service Business, 1(3), 181–193. doi:10.1007/s11628-006-0014-7

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