A Statistical Approach for Binary Vectors Modeling and Clustering

Authors:
Nizar Bouguila;Khalid Daoudi
Affiliations:
Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada H3G 2W1;CNRS-IRIT, Université Paul Sabatier, Toulouse, France F-31062
Venue:
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Year:
2009

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Abstract

This paper presents an approach for Binary feature selection. Our selection technique is based on a principled statistical model using a finite mixture of distributions. In contrast with classic feature selection algorithms that have been proposed in supervised settings, where training data are available and completely labeled, our approach is fully unsupervised. Through some applications, we found that our feature selection model improves the clustering results.