Autor(es):
Santos, Jorge 
Data: 2012
Identificador Persistente: http://hdl.handle.net/10174/7412
Origem: Repositório Científico da Universidade de Évora
Assunto(s): Data mining; process model; CRISP-DM
Detalhes do Documento
DATA MINING PROCESS MODELS: A ROADMAP FOR KNOWLEDGE DISCOVERY
Descrição
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.
Tipo de Documento
Parte ou capítulo de livro
Idioma Inglês
Editor(es) Moutinho, Luiz.; Kun-Huang, Huarng
Idioma Inglês
Editor(es) Moutinho, Luiz.; Kun-Huang, Huarng
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