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- W2002313360 abstract "ABSTRACT Some data sets contain outlying data values which can degrade the quality of the clustering results obtained using standardtechniques such as the fuzzy c-means algorithm. This note gives an extended family of fuzzy c-means type models, andattempts to empirically identify those members ofthe family which are least influenced by the presence of outliers. The formofthe extended family ofclustering criteria suggests an alternating optimization approach is feasible, and specific algorithmsfor implementing the optimization ofthe models are stated. The implemented approach is then tested using various artificialdata sets.Keywords: fuzzy clustering, Iuizzy c-means, outlying data, alternating optimization 1. INTRODUCTION Real world data sets are sometimes contaminated by outlying data points containing gross errors. Recently attention hasbeen given to robust techniques for fuzzy clustering which are relatively unaffected by outliers; three of the more interestingrobust approaches are due to Dave' ," @default.
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- W2002313360 date "1999-03-22" @default.
- W2002313360 modified "2023-09-23" @default.
- W2002313360 title "<title>Generalized fuzzy c-means clustering in the presence of outlying data</title>" @default.
- W2002313360 doi "https://doi.org/10.1117/12.342909" @default.
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