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Adapting Supervised Feature Selection Methods for Clustering Tasks
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Author(s): Eduardo Raul Hruschka (Catholic University of Santos, Brazil), Thiago F. Covões (Catholic University of Santos, Brazil), Estevam R. Hruschka Jr. (Federal University of São Carlos, Brazil)and Nelson F. F. Ebecken (Federal University of Rio de Janeiro, Brazil)
Copyright: 2007
Pages: 4
Source title:
Managing Worldwide Operations and Communications with Information Technology
Source Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-59904-929-8.ch024
ISBN13: 9781599049298
EISBN13: 9781466665378
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Abstract
In this paper, we elaborate on how feature selection methods traditionally employed in classification problems can be adapted for clustering problems, assuming that the number of clusters is not known a priori. Computational complexity of each described algorithm is provided. Empirical results in six bioinformatics datasets illustrate that the adaptation of four well-known supervised methods for feature selection (correlation-based, consistency-based, wrapper of k-NN classifier, and C4.5) can be useful for clustering tasks.
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