Fuzzified Even-sized Clustering Based on Optimization for Pairwise Constraint

Abstract

Clustering is one of methods of unsupervised learning. However, semi-supervised clustering, labeling a part of data to obtain desired result, is actively researched. On semi-supervised clustering, labeled data set is called semi-supervised dataset. a representive labeling is pairwise constraints which apply constarints to pair of instances. Above all, multiple researches about must-link constraints which demand pair of instances to be in same cluster and cannot-link constraints which demand pair of instances to be in differnt clusters are reported. incidentally, one of us proposed fuzzified even-sizedclustering based on optimization (FECBO) as one of clustering algorithms which classfies data set to same size clusters. It has been applied to delivery planning. In this paper, we propose algorithms that add pairwise constraints to FECBO and invesgate its effectiveness through numerical example.