Supervised Gravitational Clustering with Bipolar Fuzzification

Data clustering is an important part of cluster analysis. Numerous semi-supervised or supervised clustering algorithms based on various theories have been developed, and new clustering algorithms continue to appear in the literature. The problem of common supervised clustering is to train a clustering algorithm to produce desirable clusters and complete clusters over datasets and learn how to cluster future sets of objects. In this paper, we have proposed an algorithm called Supervised Gravitational Clustering based on bipolar fuzzification. Traditional supervised clustering methods identify class-uniform clusters; but the offered method identifies class-multiform clusterswith high probability densities. For this aim we have proposed two approaches: common effect and maximal effect. The first, common effect approach, calculates total effect of all class-centers over searching point. Also, this approach is basis for mapping of novel method. The second, maximal effect approach, determines class-centers with the strongest effect over searching point.