期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2013
卷号：56
期号：3
出版社：Journal of Theoretical and Applied
摘要：The automatic clustering is a useful tool for data-mining. It�s a daily necessity for the searcher whatever his specialty. Indeed because of the huge amount of information available on the web-site, the access to relevant information in a suitable time is a difficult task. By grouping those informations in clusters this problem can be surmounted. Many clustering methods exist in the literature but the efficient ones suffer from some drawbacks. The main of them follows from the initialization phase which is performed randomly. Among these algorithms we find the k-means(deterministic and probabilistic version) and the clustering method based on Gaussian mixture. In these algorithms the initial parameters including the number of cluster are chosen randomly. Consequently an improper choice leads to poor clusters. In this paper we propose an approach attempting to overcome these problems. In this method the initial parameters are automatically and suitably identified. To this end, the structure of data is investigated in each iteration. To validate the proposed method a number of experiments are performed.
