Two semi-supervised entropy regularized fuzzy c-means

Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto

Research output: Contribution to conferencePaper

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 semisupervised standard fuzzy c-means algorithm. Last, two iterative algorithms are proposed by solving the optimization problems.

Original languageEnglish
Pages61-65
Number of pages5
Publication statusPublished - 2010 Dec 1
EventJoint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010 - Okayama, Japan
Duration: 2010 Dec 82010 Dec 12

Conference

ConferenceJoint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010
CountryJapan
CityOkayama
Period10/12/810/12/12

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems

Cite this

Kanzawa, Y., Endo, Y., & Miyamoto, S. (2010). Two semi-supervised entropy regularized fuzzy c-means. 61-65. Paper presented at Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010, Okayama, Japan.