Some Pairwise Constrained Semi-Supervised Fuzzy c-Means Clustering Algorithms

Authors:
Yuchi Kanzawa;Yasunori Endo;Sadaaki Miyamoto
Affiliations:
Shibaura Institute of Technology, Tokyo, Japan 135-8548;University of Tsukuba, Japan;University of Tsukuba, Japan
Venue:
MDAI '09 Proceedings of the 6th International Conference on Modeling Decisions for Artificial Intelligence
Year:
2009

Citing 4
Cited 1

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Abstract

In this paper, some semi-supervised clustering methods are proposed with two types of pair constraints: two data have to be together in the same cluster, and two data have to be in different clusters, which are classified into two types: one is based on the standard fuzzy c -means algorithm and the other is on the entropy regularized one. First, the standard fuzzy c -means and the entropy regularized one are introduced. Second, a pairwise constrained semi-supervised fuzzy c means are introduced, which is derived from pairwise constrained competitive agglomeration. Third, some new optimization problem are proposed, which are derived from adding new loss function of memberships to the original optimization problem, respectively. Last, an iterative algorithm is proposed by solving the optimization problem.