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CRISP-DM
Cross-industry standard process for data mining
Historie
Historie
Cross-industry standard process for data mining, commonly known by its acronym CRISP-DM,[1] is a data mining process model that describes commonly used approaches that data mining experts use to tackle problems. Polls conducted at one and the same website (KDNuggets) in 2002, 2004, 2007 and 2014 show that it was the leading methodology used by industry data miners who decided to respond to the survey.[2][3][4][5] The only other data mining approach named in these polls was SEMMA. However, SAS Institute clearly states that SEMMA is not a data mining methodology, but rather a "logical organization of the functional tool set of SAS Enterprise Miner." A review and critique of data mining process models in 2009 called the CRISP-DM the "de facto standard for developing data mining and knowledge discovery projects."[6] Other reviews of CRISP-DM and data mining process models include Kurgan and Musilek's 2006 review,[7] and Azevedo and Santos' 2008 comparison of CRISP-DM and SEMMA.[8] Efforts to update the methodology started in 2006, but have As of 30 June 2015 not led to a new version, and the "Special Interest Group" (SIG) responsible along with the website has long disappeared (see History of CRISP-DM).
In 2015, IBM corporation released a new methodology called Analytics Solutions Unified Method for Data Mining/Predictive Analytics[9] (also known as ASUM-DM) which refines and extends CRISP-DM.
Business Understanding
Anwendungs-beispiele
Quellen
Quellen
Óscar Marbán / Gonzalo Mariscal / Javier Segovia: A Data Mining & Knowledge Discovery Process Model, Wien, 2009
Pete Chapman (NCR) / Julian Clinton (SPSS) / Randy Kerber (NCR) /Thomas Khabaza (SPSS) / Thomas Reinartz (DaimlerChrysler) / Colin Shearer (SPSS) / Rüdiger Wirth (DaimlerChrysler): CRISP-DM 1.0 - Step-by-step data mining guide, 1999-2000