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Proceedings Paper

Generalized fuzzy c-means clustering in the presence of outlying data
Author(s): Richard J. Hathaway; Dessa D. Overstreet; Yingkang Hu; John W. Davenport
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Paper Abstract

Some data sets contain outlying data values which can degrade the quality of the clustering results obtained using standard techniques such as the fuzzy c-means algorithm. This note gives an extended family of fuzzy c-means type models, and attempts to empirically identify those members of the family which are least influenced by the presence of outliers. The form of the extended family of clustering criteria suggests an alternating optimization approach, is feasible, and specific algorithms for implementing the optimization of the models are stated. The implemented approach is then tested using various artificial data sets.

Paper Details

Date Published: 22 March 1999
PDF: 9 pages
Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); doi: 10.1117/12.342909
Show Author Affiliations
Richard J. Hathaway, Georgia Southern Univ. (United States)
Dessa D. Overstreet, Philips Consumer Electronics (United States)
Yingkang Hu, Georgia Southern Univ. (United States)
John W. Davenport, Georgia Southern Univ. (United States)


Published in SPIE Proceedings Vol. 3722:
Applications and Science of Computational Intelligence II
Kevin L. Priddy; Paul E. Keller; David B. Fogel; James C. Bezdek, Editor(s)

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