Two Semi-supervised Entropy Regularized Fuzzy <i>c</i>-Means

• Kanzawa Yuchi

  Shibaura Institute of Technology

• Endo Yasunori

  University of Tsukuba

• Miyamoto Sadaaki

  University of Tsukuba

Abstract

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.

Journal

• SCIS & ISIS

  SCIS & ISIS 2010 (0), 61-65, 2010

  Japan Society for Fuzzy Theory and Intelligent Informatics

Keywords

• Clustering
• Fuzzy <i>c</i>-Means
• Entropy Regularization

Details

• CRID: 1390282680566618496
• NII Article ID: 130005019637
• DOI: 10.14864/softscis.2010.0.61.0
• Text Lang: en
• Data Source:
  • JaLC
  • CiNii Articles
• Abstract License Flag: Disallowed