An Experimental Study on Unsupervised Clustering-Based Feature Selection Methods

Feature selection is an essential task in data mining because it makes it possible not only to reduce computational times and storage requirements, but also to favor model improvement and better data understanding. In this work, we analyze three methods for unsupervised feature selection that are based on the clustering of features for redundancy removal. We report experimental results obtained in ten datasets that illustrate practical scenarios of particular interest, in which one method may be preferred over another. In order to provide some reassurance about the validity and non-randomness of the obtained results, we also present the results of statistical tests.