Evaluating Entropic Based Clustering Algorithms on Biomedical Data

Abstract

Clustering algorithms are being widely used on biomedical data. They aim to extract important information that can be used to improve life conditions by helping specialized technicians on the decision process. Clustering algorithms based on information theory concepts claim that by using higher order statistic they are able to extract more information from the data and therefore provide much better results. In this work we try to verify this claim by comparing the performance of some entropic clustering algorithms against more conventional ones. Results of the performed experiments are not conclusive but they seem to indicate that this kind of entropic algorithms may provide some improvements when clustering biomedical data.