Clustering methods assign objects to clusters using only as prior information the characteristics of the objects. However, clustering algorithms performance can be improved when background knowledge is available. Such background knowledge can be incorporated in a clustering method as label constraints which results in a semi-supervised clustering algorithm. We propose to extend two possibilistic clustering algorithms to make use of available a priori information. The goal is twofold: to improve the accuracy of the clustering result by leading the method towards a desired solution and to detect outliers by taking advantage of the generated possibilistic partition. The proposed methods are called semi-supervised repulsive possibilistic cmeans (SRPCM) and semi-supervised possibilistic fuzzy c-means (SPFCM). They correspond to possibilistic clustering algorithms that introduce label constraints. Experimental results show that the proposed algorithms using label constraints improve (1) the clustering result and (2) the outliers detection.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.