Research And Application Of Fuzzy Clustering Based On Genetic Algorithm

Data clustering is an emerging area which involves various areas as data mining, statistics, machine learning, spatial database technology and business information, etc. Fuzzy cluster analysis put fuzzy theory into application of cluster analysis to provide capability in data display and is widely used in various areas. FCM(Fuzzy c-means) algorithm is one of important methods in fuzzy clustering, it has the characteristics as simple, fast convergence and strong local searching power, etc. However, FCM is sensitive to initialization and tends to result in local minimum in iterations.Genetic Algorithm is a random searching global optimization algorithm, it targets all the individuals of a population and searches effectively in a coded parameter space with the random technical guidance. Because of its simple solution procedure, it becomes one of the main algorithms in intelligence computing. The combination of FCM algorithm and genetic algorithm benefits the global optimization and makes tremendous improvement in algorithm performance. However, a simple genetic algorithm only uses a fixed-probability and the mutation rate for solution which has shortcomings like slow convergence and poor stability.This paper studies the crossover and mutation probability of generic algorithm and presents a new crossover and mutation probability. A new adaptive generic algorithm fuzzy c-means(AGAFCM) which combines both generic algorithm and FCM is proposed. AGAFCM takes full advantage of the global optimization of generic algorithm and the local search power of FCM and has great improvement in both accuracy and efficiency.Performance assement is a basic part of human resources management and provide accurate feedback for human resources management. The paer puts improved adaptive generic and fuzzy cluster alogrithm into cluster analysis of employee performance assement, generating an employee performance assement model for cluster analysis and provides an effective data analysing method for modern emterprize human resource management. The model is applicated in practical human resource management and definitely proves the usage and effectiveness.The paper presents the main works and contributions as follows: 1)Improves the crossover and mutation probability of generic algorithm. It provides new cross-over and mutation probability which is adjusted according to the individual adaptive ability so as to speed up the convergence and stabability.2)Improves the generic algorithm operation and put it into the application of fuzzy cluster and proposes a adaptive generic algorithm based fuzzy clustering. The algorithm presented in this paper is proved by exprements in convergence speed, stabability and accuracy.3)Created a new employee performance model by putting the new AGAFCM into use of cluster analysis for employee performance assement and provides human resouces mangement with accurate information in analysis to overall human resource in enterprise.