Semi-supervised fuzzy clustering with pairwise-constrained competitive agglomeration

Traditional clustering algorithms usually rely on a pre-defined similarity measure between unlabelled data to attempt to identify natural classes of items. When compared to what a human expert would provide on the same data, the results obtained may be disappointing if the similarity measure employed by the system is too different from the one a human would use. To obtain clusters fitting user expectations better, we can exploit, in addition to the unlabelled data, some limited form of supervision, such as constraints specifying whether two data items belong to a same cluster or not. The resulting approach is called semi-supervised clustering. In this paper, we put forward a new semi-supervised clustering algorithm, Pairwise-Constrained Competitive Agglomeration: clustering is performed by minimizing a competitive agglomeration cost function with a fuzzy term corresponding to the violation of constraints. We present comparisons performed on a simple benchmark and on an image database.