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22 March 1999 Generalized fuzzy c-means clustering in the presence of outlying data
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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.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Richard J. Hathaway, Dessa D. Overstreet, Yingkang Hu, and John W. Davenport "Generalized fuzzy c-means clustering in the presence of outlying data", Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999);


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