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By Pascal Poncelet; Maguelonne Teisseire; Florent Masseglia

ISBN-10: 1599041626

ISBN-13: 9781599041629

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In Proceeding of the European Conference on Machine Learning, pages (pp. 192-212). Daróczy, Z. (1970). Generalized information functions. Information and Control, 16, 36-51. , & Sahami, M. (1995). Supervised and unsupervised discretization of continuous features. In Proceedings of the 12th International Conference on Machine Learning, (pp. 194-202). , Kriegel, H. , & Xu, X. (1998). Incremental clustering for mining in a data warehousing environment. In VLDB, (pp. 323-333). Fayyad, U. M. (1991).

Proportional k interval discretization for naive Bayes classifiers. In Proceedings of the 12th European Conference on Machine Learning, (pp. 564-575). , & Webb, G. I. (2003). Weighted proportional k -interval discretization for naive Bayes classifiers. In Proceedings of the PAKDD. Simovici, D. , & Jaroszewicz, S. (2000). On information-theoretical aspects of relational databases. In C. Calude & G. ), Finite versus infinite. London: Springer Verlag. , & Jain, A. (1996). Algorithms for feature selection: An evaluation.

2005), is to introduce an algorithm for feature selection that clusters attributes using a special metric, and then use a hierarchical clustering for feature selection. Hierarchical algorithms generate clusters that are placed in a cluster tree, which is commonly known as a dendrogram. Clusterings are obtained by extracting those clusters that are situated at a given height in this tree. We show that good classifiers can be built by using a small number of attributes located at the centers of the clusters identified in the dendrogram.

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Data mining patterns by Pascal Poncelet; Maguelonne Teisseire; Florent Masseglia

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