International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE)
Due to increase of the mining of compressed data from various types of applications. Clustering is the process for resolving that type of applications. Traditionally the users' are using different clustering applications for grouping elements with equal priority. And the authors represent single clustering process with multi dimensional data grouping using clustering but the only problem is performance of grouping individual elements with time queries. In this paper, they are introducing greedy heuristic algorithm for analyzing multi dimensional data representation. Their approach can be worked with efficient data sharing in the commercial data compression process. By using aggregation functions in greedy heuristic algorithm for every clustering techniques to increase the performance of divisible data representation.