Application of rough set theory to feature selection for unsupervised clustering

Feature selection is a valuable technique in data analysis for information-preserving data reduction. This paper describes the use of rough set theory (RST) to construct reducts in a supervised way for reducing the number of features in unsupervised clustering. The application to a hierarchical clustering of Pseudomonas species is presented as an example. The Wallace measure is used for the comparison of the clustering results based on the original data set and those based on the reduced data set.