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：2014
卷号：70
期号：2
出版社：Journal of Theoretical and Applied
摘要：To address the clustering problem related to multi-dimensional data clustering, a number of techniques have been implemented. A constraint based multi-dimensional data-clustering algorithm is proposed in this paper which helped with associative clustering can find out the number of clusters optimally present in a multi-dimensional data set. Now, by bays factor computation process associative constraint based clustering process is executed. Moreover, genetic algorithm is applied to optimization process to discover the optimal cluster results. The constraints based proposed algorithm assists in recognizing the right data to be clustered and the knowledge considering the data regarded as a constraint which enhances the precision of clustering. The data constraints furthermore assist in indicating the data related to the clustering task. The result of the proposed optimal associative clustering algorithm is compared with an existing algorithm on two multi dimensional datasets. Experimental result demonstrates that the proposed method is able to achieve a better clustering solution when compared with one existing algorithm.
