Two semi-supervised entropy regularized fuzzy c-means

Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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

Other

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

Fingerprint

Entropy

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. In SCIS and ISIS 2010 - Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems (pp. 61-65)

Two semi-supervised entropy regularized fuzzy c-means. / Kanzawa, Yuchi; Endo, Yasunori; Miyamoto, Sadaaki.

SCIS and ISIS 2010 - Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems. 2010. p. 61-65.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kanzawa, Y, Endo, Y & Miyamoto, S 2010, Two semi-supervised entropy regularized fuzzy c-means. in SCIS and ISIS 2010 - Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems. pp. 61-65, Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems, SCIS and ISIS 2010, Okayama, 10/12/8.
Kanzawa Y, Endo Y, Miyamoto S. Two semi-supervised entropy regularized fuzzy c-means. In SCIS and ISIS 2010 - Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems. 2010. p. 61-65
Kanzawa, Yuchi ; Endo, Yasunori ; Miyamoto, Sadaaki. / Two semi-supervised entropy regularized fuzzy c-means. SCIS and ISIS 2010 - Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems. 2010. pp. 61-65
@inproceedings{8efc0a12eea94ef6ba32c183ce878d5c,
title = "Two semi-supervised entropy regularized fuzzy c-means",
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.",
author = "Yuchi Kanzawa and Yasunori Endo and Sadaaki Miyamoto",
year = "2010",
language = "English",
pages = "61--65",
booktitle = "SCIS and ISIS 2010 - Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems",

}

TY - GEN

T1 - Two semi-supervised entropy regularized fuzzy c-means

AU - Kanzawa, Yuchi

AU - Endo, Yasunori

AU - Miyamoto, Sadaaki

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84866648466&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84866648466&partnerID=8YFLogxK

M3 - Conference contribution

SP - 61

EP - 65

BT - SCIS and ISIS 2010 - Joint 5th International Conference on Soft Computing and Intelligent Systems and 11th International Symposium on Advanced Intelligent Systems

ER -