On Feature Selection through Clustering Richard Butterworth, Gregory Piatetsky-Shapiro, and Dan Simovici   View Paper (PDF)   Return to Session 2: Clustering Schemes I Abstract We study an algorithm for feature selection that clusters attributes using a special metric and then makes use of the dendrogram of the resulting cluster hierarchy to choose the most relevant attributes. The main interest of our technique resides in the improved understanding of the structure of the analyzed data and of the relative importance of the attributes for the selection process. ©2006 Association for Computing Machinery