The goal of unsupervised learning, i.e., clustering, is to determine the intrinsic
structure of unlabeled data. Feature selection for clustering improves the performance of grouping
by removing irrelevant features. Typical feature selection algorithms select a common feature subset
for all the clusters. Consequently, clusters embedded in different feature subspaces are not able to
be identified. In this paper, we introduce a probabilistic model based on Gaussian mixture to solve
this problem. Particularly, the feature relevance for an individual cluster is treated as a probability,
which is represented by localized feature saliency and estimated through Expectation Maximization
(EM) algorithm during the clustering process. In addition, the number of clusters is determined
simultaneously by integrating a Minimum Message Length (MML) criterion. Experiments carried
on both synthetic and real-world datasets illustrate the performance of the proposed approach in
finding clusters embedded in feature subspace.