Feature Selection in Unsupervised Context: Clustering Based Approach

Abstract

In this paper we present a novel feature selection method that is applicable in unsupervised learning tasks. The method is based on clustering quality measures, which reflect different aspects of clustering performance. Sequential Floating Forward Search algorithm is employed to search through the original feature space for the best possible subset. Main stress has been put on the objectivism of the new technique, so that it could be applied in various classification tasks. Results of experiments with texture images are presented in order to confirm effectiveness of the method.