DATA MINING PROCESS MODELS: A ROADMAP FOR KNOWLEDGE DISCOVERY

Armando B. Mendes, Luís Cavique and Jorge M.A. Santos
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Armando B. Mendes: Universidade Açores, Portugal
Luís Cavique: Universidade Aberta, Portugal
Jorge M.A. Santos: Universidade Évora, Portugal

Chapter 17 in Quantitative Modelling in Marketing and Management, 2012, pp 405-433 from World Scientific Publishing Co. Pte. Ltd.

Abstract: AbstractData mining applications are common for quantitative modelling management problems resolution. As their learning curve has been very much simplified, is no surprise that many users try to apply data mining methods to data bases in a non-planned way. In this chapter, the CRISP-DM process model methodology is presented with the intention of avoiding common traps in data mining applications utilization. The use of this methodology is exemplified with serveral cases of application developed by the authors.

Keywords: Quantitative Modelling; Statistical; Computer; Marketing; Neural Networks; Fuzzy Logic; k-Clique Model; Meta-heuristics (search for similar items in EconPapers)
Date: 2012
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