Topics: Data mining, Data, Data analysis Pages: 87 (19391 words) Published: April 27, 2013
Step-by-step data mining guide Pete Chapman (NCR), Julian Clinton (SPSS), Randy Kerber (NCR), Thomas Khabaza (SPSS), Thomas Reinartz (DaimlerChrysler), Colin Shearer (SPSS) and Rüdiger Wirth (DaimlerChrysler)

SPSS is a registered trademark and the other SPSS products named are trademarks of SPSS Inc. All other names are trademarks of their respective owners. © 2000 SPSS Inc. CRISPMWP-1104

This document describes the CRISP-DM process model and contains information about the CRISP-DM methodology, the CRISP-DM reference model, the CRISP-DM user guide, and the CRISP-DM reports, as well as an appendix with additional related information. This document and information herein are the exclusive property of the partners of the CRISP-DM consortium: NCR Systems Engineering Copenhagen (USA and Denmark), DaimlerChrysler AG (Germany), SPSS Inc. (USA), and OHRA Verzekeringen en Bank Groep B.V. (The Netherlands). Copyright © 1999, 2000 All trademarks and service marks mentioned in this document are marks of their respective owners and are as such acknowledged by the members of the CRISP-DM consortium.


Foreword CRISP-DM was conceived in late 1996 by three “veterans” of the young and immature data mining market. DaimlerChrysler (then Daimler-Benz) was already ahead of most industrial and commercial organizations in applying data mining in its business operations. SPSS (then ISL) had been providing services based on data mining since 1990 and had launched the first commercial data mining workbench—Clementine®—in 1994. NCR, as part of its aim to deliver added value to its Teradata® data warehouse customers, had established teams of data mining consultants and technology specialists to service its clients’ requirements. At that time, early market interest in data mining was showing signs of exploding into widespread uptake. This was both exciting and terrifying. All of us had developed our approaches to data mining as we went along. Were we doing it right? Was every new adopter of data mining going to have to learn, as we had initially, by trial and error? And from a supplier’s perspective, how could we demonstrate to prospective customers that data mining was sufficiently mature to be adopted as a key part of their business processes? A standard process model, we reasoned, non-proprietary and freely available, would address these issues for us and for all practitioners. A year later, we had formed a consortium, invented an acronym (CRoss-Industry Standard Process for Data Mining), obtained funding from the European Commission, and begun to set out our initial ideas. As CRISP-DM was intended to be industry-, tool-, and application-neutral, we knew we had to get input from as wide a range as possible of practitioners and others (such as data warehouse vendors and management consultancies) with a vested interest in data mining. We did this by creating the CRISP-DM Special Interest Group (“The SIG,” as it became known). We launched the SIG by broadcasting an invitation to interested parties to join us in Amsterdam for a day-long workshop: We would share our ideas, invite them to present theirs, and openly discuss how to take CRISP-DM forward. On the day of the workshop, there was a feeling of trepidation among the consortium members. Would no one be interested enough to show up? Or, if they did, would they tell us they really didn’t see a compelling need for a standard process? Or that our ideas were so far out of step with others’ that any idea of standardization was an impractical fantasy? The workshop surpassed all our expectations. Three things stood out: ■ ■ ■

Twice as many people turned up as we had initially expected There was an overwhelming consensus that the industry needed a standard process and needed it now As attendees presented their views on data mining from their project experience, it became clear that although there were superficial differences—mainly in demarcation of phases and in...
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