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
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;
Conference_Titel :
Fuzzy Information Processing Society - NAFIPS, 1998 Conference of the North American
Conference_Location :
Pensacola Beach, FL
Print_ISBN :
0-7803-4453-7
DOI :
10.1109/NAFIPS.1998.715575
