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Implementation of Optimization Using Eclat and PSO for Efficient Association Rule Mining

M.Sathya, Dr.K.Thangadurai, , ,
Affiliations
PG and Research Department of Computer Science, Government Arts College (Autonomous), Karur, India,
:10.22362/ijcert/2017/v4/i1/xxxx


Abstract
In this paper, the IEPSO-ARM technique used Eclat algorithm for generating the association rules. With help of Eclat algorithm, IEPSO-ARM technique initially estimates the support value to find the frequent items in the dataset and then determines correlation value to generate the association rules. Finally, the IEPSO-ARM technique designed an Eclat based Particle Swarm Optimization (E-PSO) algorithm for generating the optimized association rule to analyze the frequently buying products by customer in supermarkets and to improve sales growth maintenance of supermarkets. The performance of IEPSO-ARM technique is tested with the metrics such as running time for frequent itemset generation, memory for association rule generation and number of rules generated.


Citation
M.Sathya, Dr.K.Thangadurai, “Implementation of Optimization Using Eclat and PSO for Efficient Association Rule Mining”, International Journal Of Computer Engineering In Research Trends, 4(1):4-8, January-2017.


Keywords : frequent item set, Eclat, PSO, association rule mining, supermarkets

References
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[2] Zhi-Hong Deng, Sheng-Long Lv, “PrePost+: An efficient N-lists-based algorithm for mining frequent itemsets via Children–Parent Equivalence pruning”, Expert Systems with Applications, Elsevier, Volume 42, Pages 5424–5432, 2015
[3] Meera Narvekara, Shafaque Fatma Syed, “An optimized algorithm for association rule mining using FP tree”, Procedia Computer Science, Elsevier, Volume 45, Pages 101 – 110,  2015 

[4] Anil Vasoya, Dr. Nitin Koli, “Mining of association rules on large database using distributed and parallel computing”, Procedia Computer Science, Elsevier, Volume 79, Pages 221 – 230, 2016 

[5] Ish Nath Jha, Samarjeet Borah, “Efficient Association Rule Mining Using Improved Apriori Algorithm”, International Journal of Scientific & Engineering Research, Volume 3, Issue 11, Pages 1-4, November-2012

[6] Manali Rajeev Raut, Hemlata Dakhore, “An Approach to Mining Association Rules in Horizontally Distributed Databases with Anonymous ID Assignment”, IEEE 2015 Global Conference on Communication Technologies (GCCT), Pages 23-24, April 2015

[7] M. Krishnamurthy, E. Rajalakshmi, R. Baskaran, A. Kannan, “Prediction of customer buying nature from frequent itemsets generation using Quine-McCluskey method”, IET Chennai Fourth International Conference on Sustainable Energy and Intelligent Systems (SEISCON 2013), Pages 12-14, 2013

[8] Jesmin Nahar, Tasadduq Imama, Kevin S. Tickle, Yi-Ping Phoebe Chen, “Association rule mining to detect factors which contribute to heart disease in males and females”, Expert Systems with Applications, Elsevier, Volume 40, Pages 1086–1093, 2013

[9] Bay VO, Frans Coenen, Bac Le, “new method for mining Frequent Weighted Itemsets based on WIT-trees”, Expert Systems with Applications, Elsevier, Expert Systems with Applications, Elsevier, Volume 40, Pages 1256–1264, 2013

[10] D. Magdalene Delighta Angeline, “Association Rule Generation for Student Performance Analysis using Apriori Algorithm”, The SIJ Transactions on Computer Science Engineering & its Applications (CSEA), Volume 1, Issue 1, Pages 12-16, March-April 2013

[11] Dr. S. Vijayarani and Ms. R. Prasannalakshmi, “Comparative Analysis of Association Rule Generation Algorithms in Data Streams”, International Journal on Cybernetics & Informatics (IJCI), Volume 4, Issue 1, Pages 15-25, February 2015  
[12] J.Suresh, P.Rushyanth,Ch.Trinath, “Generating associations rule mining using Apriori and FPGrowth Algorithms”, International Journal of Computer Trends and Technology (IJCTT), volume4, Issue4, Pages 887-892, April 2013 
[13] Ruchi Bhargava, Prof. Shrikant Lade, “Effective Positive Negative Association Rule Mining Using Improved Frequent Pattern”, International Journal of Modern Engineering Research (IJMER), Volume 3, Issue 2, Pages 1256-1262, March-April. 2013 
[14] Sample Dataset for Market Basket Analysis:http://recsyswiki.com/wiki/Grocery_shoppi  


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DOI:10.22362/ijcert