Mutual information evaluation: A way to predict the performance of feature weighting on clustering

Abstract

Feature weighting is one of the popular and effective ways to improve clustering quality. How to choose a proper weighting method for a data object is widely recognized as a difficult problem. Among majority of weighting schemes and combination weighting methods, the traditional way is evaluating the performance of feature weighting by measuring the quality of clustering. However, it is a time-consuming task because clustering algorithms should be run many times, and the number of times depends on the number of weighting schemes or the number of combination weighting iteration. To address the issue, we propose to apply the Mutual Information to predict the performance of feature weighting. We propose to judge the quality of feature weighting by the resulting gain in mutual information. Therefore, the top s weighted data representations can be selected from the weighting data representation set. Then, the best/second best cluster result can be obtained from the top s representations. Experimental results show that the Mutual Information evaluation reduces the running time without sacrificing the quality of clustering.