Efficient Feature Selection in Conceptual Clustering

Abstract

Feature selection has proven to be a valuable technique in supervised learning for improving predictive accuracy while reducing the number of attributes considered in a task. We investigate the potential for similar benefits in an unsupervised learning task, conceptual clustering. The issues raised in feature selection by the absence of class labels are discussed and an implementation of a sequential feature selection algorithm based on an existing conceptual clustering system is described. Additionally, we present a second implementation which employs a technique for improving the efficiency of the search for an optimal description and compare the performance of both algorithms. 1 Introduction The choice of which attributes to use in describing a given input has crucial impact on the classes induced by a learner. For this reason, the majority of real-world data sets used in inductive learning research have been constructed by domain experts and contain only those attributes which are...