A genetics-based approach to fuzzy clustering

The traditional fuzzy objective-function-based clustering algorithms, the fuzzy c-means (FCM) algorithm and the FCM-type algorithms, are in essence local search techniques that search for the optimum by using a hill-climbing technique. Thus, they often fail in the search for global optimum. In this paper, we combine the genetic algorithms with traditional clustering algorithms to obtain a better clustering performance. Our experimental results show that the proposed genetic-based clustering algorithms have much higher probabilities of finding the global or near-global optimal solutions than the traditional algorithms.<>