An Automatic Data Clustering Algorithm based on Differential Evolution

• Main Authors: Chiech-an Tai, 戴杰安
• Other Authors: Ming-Chao Chiang
• Format: Others
• Language: en_US
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/y68n4c

碩士 === 國立中山大學 === 資訊工程學系研究所 === 102 === As one of the traditional optimization problems, clustering still plays a vital role for the re-searches both theoretically and practically nowadays. Although many successful clustering algorithms have been presented, most (if not all) need to be given the number of clusters before the clustering procedure is invoked. A novel differential evolution based clustering algorithm is presented in this paper to solve the problem of automatically determining the number of clusters. The proposed algorithm, called enhanced differential evolution for automatic cluster-ing (EDEAC), leverages the strengths of two technologies: a novel histogram-based analysis technique for finding the approximate number of clusters and a heuristic search algorithm for fine-tuning the automatic clustering results. The experimental results show that the proposed algorithm can not only determine the approximate number of clusters automatically, but it can also provide an accurate number of clusters rapidly even for high dimensional datasets com-pared to other existing automatic clustering algorithms.