An Automatic Clustering Method Based On Evolutionary Programming

Along with the rapid development of modern information technology such as the Internet, people must face massive information everyday. How to extract useful information from massive data has increasingly become a hot topic of concern. As a basic means of information processing, cluster analysis technology has become people's concern in recent years. Cluster analysis has also gained a wide range of research and application in machine learning, pattern recognition, data mining, information retrieval and many other fields, .The clustering algorithm mainly includes partition-based clustering algorithms and hierarchical clustering algorithm. Partition-based clustering algorithms are the most commonly used data mining algorithms, As an important partition-based clustering algorithm, fuzzy C-Means clustering algorithm (FCM) is widely used in practice. However, there are three drawbacks in fuzzy c-means clustering algorithm: the number of cluster centers must be specified in advance; the algorithms tend to converge to the local minimum or saddle point; clustering results are impacted much by initial cluster centers.In order to solve these flaws, this paper presents automatic clustering algorithm based on evolutionary programming (EPFCM). With the optimization ability of evolutionary programming and evaluation characteristics of cluster validity index in our algorithm, users don't need to specify the number of cluster centers. EPFCM can automatically search the best number of centers and the optimal cluster structure. To speed up the convergence process, we take FCM iteration into the evolutionary programming processExperiments show that EPFCM algorithm can gain best cluster centers and optimal cluster structures, and the probability of falling into local minima is greatly reduced.