DATA MINING PROCESS MODELS: A ROADMAP FOR KNOWLEDGE DISCOVERY
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 method...
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| Formato: | bookPart |
| Idioma: | eng |
| Publicado em: |
2013
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10174/7412 |
| País: | Portugal |
| Oai: | oai:dspace.uevora.pt:10174/7412 |
| Resumo: | 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. |
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