International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064


Downloads: 110 | Views: 190

Research Paper | Computer Science & Engineering | India | Volume 3 Issue 6, June 2014 | Rating: 6.8 / 10


Generalized and Identify the Best Association Rules using Genetic Algorithm

Arvind Jaiswal


Abstract: Data mining is the analysis step of the Knowledge Discovery in Databases; It is the process that results in the detection of new patterns in large data sets. In data mining association rule is a popular and easy method to find frequent itemsets from large datasets. In general frequent itemsets are generated from large data sets by applying association rule mining take too much computer time to compute all the frequent itemsets. By using Genetic Algorithm (GA) we can improve the results of association rule mining. Our main purpose is by Genetic Algorithm to generate high quality Association Rules; by which we can get four data qualities like accuracy; comprehensibility; interestingness and completeness. We formulate a general Association rule mining model for extracting useful information from very large databases. An interactive Association rule mining system is designed using a combination of genetic algorithms and a modified a-priori based algorithm. The combination of genetic algorithms with a-priori query optimization make association rule mining yield fast results. The main aim of this paper is to use the same combination to extend it to a much more general context allowing efficient mining of very large databases for many different kinds of patterns. In this paper we are using the large dataset and our Experimental results on this dataset show the effectiveness of our approach. This paper provides the show how the idea can be used either in a general purpose mining system or in a next generation of conventional query optimizers.


Keywords: Genetic Algorithm GA, Association Rule, Frequent itemset, Support, Confidence, Data Mining


Edition: Volume 3 Issue 6, June 2014,


Pages: 2188 - 2193


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