Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.2/2794
Título: Data mining process models: a roadmap for knowledge discovery
Autor: Mendes, Armando Brito
Cavique, Luís
Santos, Jorge M. Azevedo
Palavras-chave: Data mining
knowledge discovery
Data: 2012
Editora: WORLD SCIENTIFIC
Resumo: Extracting knowledge from data is the major objective of any data analysis process, including the ones developed in several sciences as statistics and quantitative methods, data base \ data warehouse and data mining. From the latter disciplines the data mining is the most ambitious because intends to analyse and extract knowledge from massive often badly structured data with many specific objectives. It is also used for relational data base data, network data, text data, log file data, and data in many other forms. In this way, is no surprise that a myriad of applications and methodologies have been and are being developed and applied for data analysis functions, where CRISP-DM (cross industry standard process for data mining) and SEMMA (sample, explore, modify, model, assessment) are two examples. The need for a roadmap is, therefore, highly recognised in the field and almost every software company has established their own process model.
Peer review: yes
URI: http://hdl.handle.net/10400.2/2794
ISBN: 978-981-4407-71-7
Aparece nas colecções:Ciências e Tecnologia - Capítulos/artigos em livros internacionais / Book chapters/papers in international books

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