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arXiv · math/0610757

Selection of variables for cluster analysis and classification rules

Ricardo Fraiman·Ana Justel·Marcela Svarc
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Abstract

In this paper we introduce two procedures for variable selection in cluster analysis and classification rules. One is mainly oriented to detect the noisy non-informative variables, while the other deals also with multicolinearity. A forward-backward algorithm is also proposed to make feasible these procedures in large data sets. A small simulation is performed and some real data examples are analyzed.

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Ricardo Fraiman, Ana Justel, Marcela Svarc. 2006-10-25. Selection of variables for cluster analysis and classification rules. https://doi.org/10.1198/016214508000000544

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