Feature selection for clustering using instance-based learning by exploring the nearest and farthest neighbors

Abstract

Feature selection for clustering is an active research topic and is used to identify salient features that are helpful for data clustering. While partitioning a dataset into clusters, a data instance and its nearest neighbors will belong to the same cluster, and this instance and its farthest neighbors will belong to different clusters. We propose a new Feature Selection method to identify salient features that are useful for maintaining the instance’s Nearest neighbors and Farthest neighbors (referred to here as FSNF). In particular, FSNF uses the mutual information criterion to estimate feature salience by considering maintainability. Experiments on benchmark datasets demonstrate the effectiveness of FSNF within the context of cluster analysis.