DocumentCode
3109951
Title
Random vector clustering using fuzzy c-means
Author
Hathaway, Richard J. ; Rogers, G. Wesley ; Bezdek, James C.
Author_Institution
Dept. of Math. & Comput. Sci. Dept., Georgia Southern Univ., GA, USA
fYear
1998
fDate
20-21 Aug 1998
Firstpage
251
Lastpage
255
Abstract
The fuzzy c-means (FCM) clustering algorithm has long been used to cluster numerical data. Recently FCM has also been used to cluster data sets consisting of mixtures of numerical, interval, and fuzzy data. Here the range of applicability of FCM is shown to include data sets whose feature values are continuous random variables. Parametric and nonparametric approaches are given and demonstrated using a simple computational example
Keywords
data analysis; fuzzy set theory; pattern recognition; random processes; FCM clustering algorithm; continuous random variables; data sets; feature values; fuzzy c-means; fuzzy data; nonparametric approaches; numerical data; parametric approaches; random vector clustering; simple computational example; Clustering algorithms; Computer science; Decoding; Fuzzy sets; Length measurement; Marine animals; Particle measurements; Partitioning algorithms; Prototypes; Random variables;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Information Processing Society - NAFIPS, 1998 Conference of the North American
Conference_Location
Pensacola Beach, FL
Print_ISBN
0-7803-4453-7
Type
conf
DOI
10.1109/NAFIPS.1998.715575
Filename
715575
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