Efficient Feature Selection in Conceptual Clustering

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Efficient Feature Selection in Conceptual Clustering
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 existingconceptual clusteringsystem 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.
Mark Devaney, Ashwin Ram
Added 26 Aug 2010
Updated 26 Aug 2010
Type Conference
Year 1997
Where ICML
Authors Mark Devaney, Ashwin Ram
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