Author(s):
Santos, Jorge 
Date: 2012
Persistent ID: http://hdl.handle.net/10174/7412
Origin: Repositório Científico da Universidade de Évora
Subject(s): Data mining; process model; CRISP-DM
Document details
DATA MINING PROCESS MODELS: A ROADMAP FOR KNOWLEDGE DISCOVERY
Description
Data 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.
Data 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.
Document Type
Part of book or chapter of book
Language English
Editor(s) Moutinho, Luiz.; Kun-Huang, Huarng
Language English
Editor(s) Moutinho, Luiz.; Kun-Huang, Huarng
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