Journal of Theoretical and Applied Information Technology 20 th December 2014. Vol.70 No.2 © 2005 - 2014 JATIT & LLS. All rights reserved . ISSN: 1992-8645 www.jatit.org E-ISSN: 1817-3195 241 AN ALGORITHM TO CONSTRAINTS BASED MULTI- DIMENSIONAL DATA CLUSTERING AIDED WITH ASSOCIATIVE CLUSTERING 1 B.KRANTHI KIRAN, 2 Dr. A VINAYA BABU 1 Assistant Professor, Department of Computer Science and Engineering, JNTUHCEJ, Karimnagar, Telangana, India 2 Professor, Department of Computer Science and Engineering JNTUniversity Hyderabad, Telangana, India 1 kranthikiran9@gmail.com, 2 avb1222@jntuh.ac.in ABSTRACT 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. Keywords: Associative Clustering, Genetic Algorithm, Multi-dimensional Data, Bays Factor, Contingency Table 1. INTRODUCTION In finding out knowledge unseen in databases, Data mining develops as a promising solution. Data Mining has been properly termed as “the non-trivial extraction of implicit, formerly unidentified and potentially constructive information from data in databases” [1], [2]. Data mining has been exploited for multiple needs both in the private and public sectors. Accurate usage of data mining contain market segmentation, fraud detection, direct marketing, interactive marketing, market basket analysis, trend analysis and more [3, 4,5,7]. In several pervasive allocated computing environments, advances in computing and communication over wired and wireless networks have resulted. These environments frequently come with dissimilar distributed sources of data and computation. Mining in such environments obviously calls for correct utilization of these allocated resources. Most off-the-shelf data mining systems are planned to work as a monolithic centralized application on the other hand. They usually download the related data to a centralized location and next execute the data mining operations [1-7]. This centralized approach does not effort well in many of the emerging allocated, ubiquitous, probably privacy-sensitive data mining applications. In order to address this problem of mining data, Distributed Data Mining (DDM) proposes an alternate approach by distributed resources [6]. For above forty years, Clustering [16, 26] has been studied widely in data mining field and across several disciplines due to its broad applications. Clustering is the process of allocating data objects into a set of disjoint groups called clusters so that objects in each cluster are more related to each other than objects from dissimilar clusters. For competent clustering of data, the literature offers with a vast number of algorithms. These algorithms can be classified into nearest- neighbor clustering, fuzzy clustering, partitional clustering, hierarchical clustering, artificial neural networks for clustering, statistical clustering algorithms, density-based clustering algorithm and