About cookies on this site Our websites require some cookies to function properly (required). In addition, other cookies may be used with your consent to analyze site usage, improve the user experience and for advertising. For more information, please review your options. By visiting our website, you agree to our processing of information as described in IBM’sprivacy statement. To provide a smooth navigation, your cookie preferences will be shared across the IBM web domains listed here.
Publication
ICDM 2005
Conference paper
Semi-supervised clustering with metric learning using relative comparisons
Abstract
Semi-supervised clustering algorithms partition a given data set using limited supervision from the user. In this paper, we propose a clustering algorithm that uses supervision in terms of relative comparisons, viz., x is closer to y than to 2. The success of a clustering algorithm also depends on the kind of dissimilarity measure. The proposed clustering algorithm learns the underlying dissimilarity measure while finding compact clusters in the given data set. Through our experimental studies on high-dimensional textual data sets, we demonstrate that the proposed algorithm achieves higher accuracy than the algorithms using pair-wise constraints for supervision. © 2005 IEEE.