Abstract
This chapter addresses the topic of knowledge discovery in heterogeneous environments. It begins with an overview of the knowledge-discovery process. Because of the importance of using clean, consistent data in the knowledge-discovery process, the chapter focuses on the problems of data integration and cleansing by presenting a framework of semantic conflicts identification and an algorithm for their resolution. The chapter then describes the various data-mining tasks that can be performed on the cleansed data, such as association rules, sequential patterns, classification and clustering. It also discusses data-mining models and algorithms, such as those related to neural networks, rule induction, decision trees, K-nearest neighbors, and genetic algorithms. The chapter concludes with a summary.
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© 2002 Springer Science+Business Media Dordrecht
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Kamel, M.N., Ceruti, M.G. (2002). Knowledge Discovery in Heterogeneous Environments. In: Bestougeff, H., Dubois, JE., Thuraisingham, B. (eds) Heterogeneous Information Exchange and Organizational Hubs. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-1769-4_13
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DOI: https://doi.org/10.1007/978-94-017-1769-4_13
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-6030-3
Online ISBN: 978-94-017-1769-4
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