Title :
Optimization of Fuzzy C-Means Clustering by Genetic Algorithms Based on Sizable Chromosome
Author :
Wang, Jie-sheng ; Gao, Xian-wen
Author_Institution :
Sch. of Electron. & Inf. Eng., Liaoning Univ. of Sci. & Technol., Anshan, China
Abstract :
Aiming at the predifined clustering number, strong randomness and easiness to fall into local optimum, a new self-adaptive FCM algorithm based on genetic algorithm is proposed. The number of fuzzy clustering and cluster centers are optimized by sizable-chromosome genetic algorithms (SC-GAs). Cut operator and splice operator are adopted to combination the chromosome to form new individuals. Non-uniform mutation operator is used to enhance the population diversity. The new proposed method can obtain the global optimam compared to standard FCM algorithm. The simulation experimental results with IRIS demonstrate the feasibility and effectiveness of the new algorithm.
Keywords :
genetic algorithms; pattern clustering; cut operator; fuzzy C-means clustering; fuzzy optimization; genetic algorithms; nonuniform mutation operator; sizable chromosome; splice operator; Automation; Biological cells; Clustering algorithms; Genetic algorithms; Genetic mutations; Iris; Virtual colonoscopy; Wheels;
Conference_Titel :
Pattern Recognition, 2009. CCPR 2009. Chinese Conference on
Conference_Location :
Nanjing
Print_ISBN :
978-1-4244-4199-0
DOI :
10.1109/CCPR.2009.5344155