Abstract

While there exist many approaches to clustering, the important issue of feature selection, that is, what attributes of the data are relevant, is rarely addressed. Feature selection for clustering is made difficult by the absence of class labels to guide the search. In this paper, we propose two approaches to deal with this problem. In the first one, instead of making hard selections, we estimate how salient each features is. An expectation-maximization (EM) algorithm is derived for this task. The second approach extends Koller and Sahami's mutual-information-based feature relevance criterion to the unsupervised case. Implementation is carried out by a backward search scheme. The resulting algorithm can be classified as a "wrapper", since it wraps mixture estimation in an outer layer that performs feature selection. Experimental results on synthetic and real data show that both methods have promising performance. 1

Citations