Identifying Best Association Rules and Their Optimization using Genetic Algorithm
Arvind Jaiswal1, Gaurav Dubey2

1Arvind Jaiswal, M.Tech (CSE), Amity School of Engineering and Technologu, Amity University, NOIDA, Uttar Pradesn, India.
2Gaurav Dubey, Asst. Prof, Amity School of Computer Science, Amity University, NOIDA, Uttar Pradesn, India.

Manuscript received on May 11, 2013. | Revised Manuscript received on May 15, 2013. | Manuscript published on May 25, 2013. | PP: 91-96 | Volume-1 Issue-7, May 2013. | Retrieval Number: G0315051713/2013©BEIESP

Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (

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. The main aim of data mining is to pull out knowledge from an existing dataset and transform it into a flexible structure. 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. Genetic Algorithms are powerful and widely applicable stochastic search and optimization methods based on the concepts of natural selection and natural evaluation. The advantage of using genetic algorithm is to discover high level prediction rules is that they perform a global search and cope better with attribute interaction than the greedy rule induction algorithm often used in data mining. The main aim of this paper is to find all the frequent itemsets from given data sets using genetic algorithm. 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 major improvement in the approaches for association rule mining using genetic algorithms.
Keywords: Genetic Algorithm (GA), Association Rule, Frequent itemset, Support, Confidence, Data Mining.