A New Possibilistic Clustering Method: The Possibilistic K-Modes

摘要

This paper investigates the problem of clustering data pervaded by uncertainty. Dealing with uncertainty, in particular, using clustering methods can be of great interest since it helps to make a better decision. In this paper, we combine the k-modes method within the possibility theory in order to obtain a new clustering approach for uncertain categorical data; more precisely we develop the so-called possibilistic k-modes method (PKM) allowing to deal with uncertain attribute values of objects where uncertainty is presented through possibility distributions. Experimental results show good performance on well-known benchmarks.