Summary
International Symposium on Nonlinear Theory and its Applications
2009
Session Number:C1L-B
Session:
Number:C1L-B3
A Semi-Supervised Entropy Regularized Fuzzy c-Means
Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto,
pp.-
Publication Date:2009/10/18
Online ISSN:2188-5079
Summary:
In this paper, two semi-supervised clustering methods are proposed, which are based on entropy regularized fuzzy c-means algorithm. First, two fuzzy c-means algorithms are introduced. The one is the standard one and the other is the entropy regularized one. Second, two semi-supervised standard fuzzy c-means algorithms are introduced, which are derived from adding loss function of memberships to the original optimization problem. Third, two new optimization problems are proposed, in which one is derived from adding new loss function of memberships to the original optimization problem and the other is derived from adding the loss function used in the latter semi-supervised standard fuzzy c-means algorithm. Last, two iterative algorithms are proposed by solving the optimization problems.