An Ensemble Feature Ranking Algorithm for Clustering Analysis

Abstract

Feature ranking is a widely used feature selection method. It uses importance scores to evaluate features and selects those with high scores. Conventional unsupervised feature ranking methods do not consider the information on cluster structures; therefore, these methods may be unable to select the relevant features for clustering analysis. To address this limitation, we propose a feature ranking algorithm based on silhouette decomposition. The proposed algorithm calculates the ensemble importance scores by decomposing the average silhouette widths of random subspaces. By doing so, the contribution of a feature in generating cluster structures can be represented more clearly. Experiments on different benchmark data sets examined the properties of the proposed algorithm and compared it with the existing ensemble-based feature ranking methods. The experiments demonstrated that the proposed algorithm outperformed its existing counterparts.