Notable uses




Data mining is used wherever there is digital data available today. Notable examples of data mining can be found throughout business, medicine, science, and surveillance.

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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 standard (JDM 1.0). Development on successors to 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 JDM 2.0 was withdrawn without reaching a final draft.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 use in predictive analytics—the key standard is the Predictive Model Markup Language (PMML), which is an XML-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 cover (for example) subspace clustering have been proposed independently of the DMG.
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Research

The premier professional body in the field is the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining (SIGKDD). Since 1989, this ACM SIG has hosted an annual international conference and published its proceedings, and since 1999 it has published a biannual academic journal titled "SIGKDD Explorations". Computer science conferences on data mining include: CIKM Conference – ACM Conference on Information and Knowledge Management European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases KDD Conference – ACM SIGKDD Conference on Knowledge Discovery and Data Mining Data mining topics are also present on many data management/database conferences such as the ICDE Conference, SIGMOD Conference and International Conference on Very Large Data Bases
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Process

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The knowledge discovery in databases (KDD) process is commonly defined with the stages: Selection Pre-processing Transformation Data mining Interpretation/evaluation. It exists, however, in many variations on this theme, such as the Cross-industry standard process for data mining (CRISP-DM) which defines six phases: Business understanding Data understanding Data preparation Modeling Evaluation Deployment or a simplified process such as (1) Pre-processing, (2) Data Mining, and (3) Results Validation. Polls conducted in 2002, 2004, 2007 and 2014 show that the CRISP-DM methodology is the leading methodology used by data miners. The only other data mining standard named in these polls was SEMMA. However, 3–4 times as many people reported using CRISP-DM. Several teams of researchers have published reviews of data mining process models, and Azevedo and Santos conducted a comparison of CRISP-DM and SEMMA in 2008. Pre-processing edit Before data mining algorithms can be used, a targe...
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