Feature Selection in Clustering Problems

Abstract

A novel approach to combining clustering and feature selection is pre- sented. It implements a wrapper strategy for feature selection, in the sense that the features are directly selected by optimizing the discrimina- tive power of the used partitioning algorithm. On the technical side, we present an efficient optimization algorithm with guaranteed local con- vergence property. The only free parameter of this method is selected by a resampling-based stability analysis. Experiments with real-world datasets demonstrate that our method is able to infer both meaningful partitions and meaningful subsets of features.