Fuzzy semi-supervised clustering with target clusters using different additional terms

Abstract

This paper discusses a method of semi-supervised fuzzy clustering with target clusters. The method uses two kinds of additional terms to ordinary fuzzy c-means objective function. One term consists of the sum of squared differences between the target cluster memberships and the membership of the solution, whereas second term has the sum of absolute differences of those memberships. While the former has a closed formula for the membership solution, the second requires a complicated algorithm. However, numerical example show that the latter method of the absolute differences works better.