Study On 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 important partition-based clustering algorithms, K-Means fuzzy C-Means clustering algorithm (FCM) is widely used in practice. However, there are three drawbacks in the algorithms: 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.Aimed at solving these flaws, this paper presents K-means clustering method based on evolutionary programming and fuzzy C-means clustering method based on evolutionary programming which is named KEP and EPFCM algorithm,By the optimization ability of evolutionary programming, KEP can avoid the flaw trapping in local minima and the impact of initial cluster centers. Experiments show that KEP have better clustering results compared with K-means and it is more faster and more precise than K-means algorithm based on GA(KGA).In EPFCM, using cluster validity index for the assessment and optimization ability of the optimization ability of evolutionary programming, 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 process; To search the optimal number of cluster centers, we modify the number of centers dynamically. Experiments show that EPFCM algorithm can gain best cluster centers and optimal cluster structures, and the probability of falling into local minima is greatly reduced.