Cross Industry Standard Process For Data Mining (CRISP-DM) Process Pattern
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A Cross Industry Standard Process For Data Mining (CRISP-DM) Process Pattern is a data science process pattern.
- Context:
- It can include steps such as Business Understanding, Data Understanding, Data Preparation, Data Pattern Modeling, Model Evaluation, and Model Deployment.
- …
- Counter-Example(s):
- See: Data Mining Ontology, Design Pattern, Data Mining Process Model.
References
2014
- (Wikipedia, 2014) ⇒ http://en.wikipedia.org/wiki/Cross_Industry_Standard_Process_for_Data_Mining Retrieved:2014-7-13.
- CRISP-DM (Cross Industry Standard Process for Data Mining) [1] is a data mining process model that describes commonly used approaches that data mining experts use to tackle problems. Polls conducted in 2002, 2004, and 2007 show that it is the leading methodology used by data miners.[2] [3] [4] The only other data mining standard named in these polls was SEMMA. However, 3-4 times as many people reported using CRISP-DM. 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."[5] Other reviews of CRISP-DM and data mining process models include Kurgan and Musilek's 2006 review,[6] and Azevedo and Santos' 2008 comparison of CRISP-DM and SEMMA.[7]
- ↑ Shearer C., The CRISP-DM model: the new blueprint for data mining, J Data Warehousing (2000); 5:13 — 22.
- ↑ Gregory Piatetsky-Shapiro (2002); KDnuggets Methodology Poll
- ↑ Gregory Piatetsky-Shapiro (2004); KDnuggets Methodology Poll
- ↑ Gregory Piatetsky-Shapiro (2007); KDnuggets Methodology Poll
- ↑ Óscar Marbán, Gonzalo Mariscal and Javier Segovia (2009); A Data Mining & Knowledge Discovery Process Model. In Data Mining and Knowledge Discovery in Real Life Applications, Book edited by: Julio Ponce and Adem Karahoca, ISBN 978-3-902613-53-0, pp. 438-453, February 2009, I-Tech, Vienna, Austria.
- ↑ Lukasz Kurgan and Petr Musilek (2006); A survey of Knowledge Discovery and Data Mining process models. The Knowledge Engineering Review. Volume 21 Issue 1, March 2006, pp 1 - 24, Cambridge University Press, New York, NY, USA doi: 10.1017/S0269888906000737.
- ↑ Azevedo, A. and Santos, M. F. (2008);KDD, SEMMA and CRISP-DM: a parallel overview. In: Proceedings of the IADIS European Conference on Data Mining 2008, pp 182-185.