Title :
Genetic fuzzy clustering
Author :
Hall, L.O. ; Bezdek, J.C. ; Boggavarpu, S. ; Bensaid, A.
Author_Institution :
Dept. of Comput. Sci. & Eng., Univ. of South Florida, Tampa, FL, USA
Abstract :
This paper describes a genetic guided fuzzy clustering algorithm. The fuzzy-c-means functional Jm is used as the fitness function. In two domains the approach is shown to avoid some higher values of Jm to which the fuzzy-c-means algorithm will converge under some initializations. Hence, the genetic guided approach shows promise as a clustering tool
Keywords :
fuzzy logic; genetic algorithms; fitness function; fuzzy-c-means functional; genetic fuzzy clustering; Clustering algorithms; Computer science; Genetic algorithms; Genetic engineering; Image converters; Image segmentation; Iterative algorithms; Minimization methods; Optimization methods; Prototypes;
Conference_Titel :
Fuzzy Information Processing Society Biannual Conference, 1994. Industrial Fuzzy Control and Intelligent Systems Conference, and the NASA Joint Technology Workshop on Neural Networks and Fuzzy Logic,
Conference_Location :
San Antonio, TX
Print_ISBN :
0-7803-2125-1
DOI :
10.1109/IJCF.1994.375077
