Localized feature selection for clustering
In clustering, global feature selection algorithms attempt to select a common feature subset that is relevant to all clusters. Consequently, they are not able to identify individual clusters that exist in different feature subspaces. In this paper, we propose a localized feature selection algorithm for clustering. The proposed algorithm computes adjusted and normalized scatter separability for individual clusters. A sequential backward search is then applied to find the optimal (maybe local) feature subsets for each cluster. Our experimental results show the need for feature selection in clustering and the benefits of selecting features locally.
feature selection clustering scatter separability unsupervised learning