Initialization method for multi-type prototype fuzzy clustering

Xinbo Gao, Zhong Xue, Jie Li, Weixin Xie

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

Abstract

Fuzzy clustering is an important branch of unsupervised classification, and has been widely used in pattern recognition and image processing. However, most of existing fuzzy clustering algorithms are sensitive to initialization, and strongly depend on the number of clusters, which limits their applications. Moreover, it also needs to know the type and number of prototypes in advance in multi-type prototype fuzzy clustering. To overcome these limitations, a method for acquiring a priori knowledge about clustering prototype is proposed in this paper, which obtain better performance in initializing multi-type prototype fuzzy clustering.

Original languageEnglish
Title of host publicationInternational Conference on Signal Processing Proceedings, ICSP
Place of PublicationPiscataway, NJ, United States
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1205-1208
Number of pages4
Volume2
StatePublished - Dec 1 1998
EventProceedings of the 1998 4th International Conference on Signal Processing Proceedings, ICSP '98 - Beijing, China
Duration: Oct 12 1998Oct 16 1998

Other

OtherProceedings of the 1998 4th International Conference on Signal Processing Proceedings, ICSP '98
CityBeijing, China
Period10/12/9810/16/98

ASJC Scopus subject areas

  • Signal Processing

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