Subspace Clustering and Some Soft Variants

Abstract

Subspace clustering is an unsupervised machine learning task that, as clustering, decomposes a data set into subgroups that are both distinct and compact, and that, in addition, explicitly takes into account the fact that the data subgroups live in different subspaces of the feature space. This paper provides a brief survey of the main approaches that have been proposed to address this task, distinguishing between the two paradigms used in the literature: the first one builds a local similarity matrix to extract more appropriate data subgroups, whereas the second one explicitly identifies the subspaces, so as to dispose of more complete information about the clusters. It then focuses on soft computing approaches, that in particular exploit the framework of the fuzzy set theory to identify both the data subgroups and their associated subspaces.