Automatic Genetic Fuzzy c-Means

Journal of Intelligent Systems

Elmoujahid Abdelaziz¹  Ettouhami Aziz¹  Jebari Khalid² 

[1] LCS Laboratory, Faculty of Sciences, Department of Physics, Mohamed V University, Rabat, Morocco;Technologies and Sciences Faculty Tangier, Department of Computer Sciences, Tangier, Morocco;

关键词: genetic algorithms;    unsupervised learning;    fuzzy clustering;    evolutionary algorithms;    gravitational search;    differential evolution;   

DOI  :  10.1515/jisys-2018-0063

来源: DOAJ

【 摘 要 】

Fuzzy c-means is an efficient algorithm that is amply used for data clustering. Nonetheless, when using this algorithm, the designer faces two crucial choices: choosing the optimal number of clusters and initializing the cluster centers. The two choices have a direct impact on the clustering outcome. This paper presents an improved algorithm called automatic genetic fuzzy c-means that evolves the number of clusters and provides the initial centroids. The proposed algorithm uses a genetic algorithm with a new crossover operator, a new mutation operator, and modified tournament selection; further, it defines a new fitness function based on three cluster validity indices. Real data sets are used to demonstrate the effectiveness, in terms of quality, of the proposed algorithm.

【 授权许可】

Unknown