Fuzzy c-means for data with tolerance by introducing penalty term

Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto

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

Abstract

In this paper, two new clustering algorithms are proposed for data with some errors. In any of these algorithms, the error is interpreted as one of decision variables - called "tolerance" - of a certain optimization problem like the previously proposed algorithm, but the tolerance in new methods is determined by the new introduced penalty term of it in the corresponding objective function. Through some numerical experiments, the difference between our methods andthe previously proposed one is discussed.

Original languageEnglish
Title of host publicationSMCia/08 - Proceedings of the 2008 IEEE Conference on Soft Computing on Industrial Applications
Pages371-376
Number of pages6
DOIs
Publication statusPublished - 2008
Event2008 IEEE Conference on Soft Computing on Industrial Applications, SMCia/08 - Muroran
Duration: 2008 Jun 252008 Jun 27

Other

Other2008 IEEE Conference on Soft Computing on Industrial Applications, SMCia/08
CityMuroran
Period08/6/2508/6/27

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Software
  • Industrial and Manufacturing Engineering

Cite this

Kanzawa, Y., Endo, Y., & Miyamoto, S. (2008). Fuzzy c-means for data with tolerance by introducing penalty term. In SMCia/08 - Proceedings of the 2008 IEEE Conference on Soft Computing on Industrial Applications (pp. 371-376). [5045992] https://doi.org/10.1109/SMCIA.2008.5045992