An Optimized Association Rule Generation for Market Basket Analysis

M Sathya, K Thangadurai

Abstract


Data mining has been an influential technique used for analyzing and summarizing data it into useful information. Frequent item set retrieval is a popular data mining technique to study customer behavior in retail stores and medical fields. Several business applications found to benefit from market access. But, there is a need for new effective method to optimize the frequent itemsets and association rule generation. In addition, frequent itemset mining data is utilized to improve the market basket access, to determine the customer behavior, details with suggestion and then to improve point of sales in supermarkets. In this paper we examine Optimization 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 this technique is tested with the metrics such as running time for frequent itemset generation, memory for association rule generation and number of rules generated.

Full Text:

PDF

References


Zhi-Hong Deng, Sheng-Long Lv, “Fast mining frequent itemsets using Nodesets”, Expert Systems with Applications, Elsevier, Expert Systems with Applications, Elsevier, Volume 41, Pages 4505–4512, 2014

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

Michalis Mavrovouniotis, Shengxiang Yang, “Ant colony optimization with immigrants schemes for the dynamic travelling salesman problem with traffic factors”, Elsevier, Applied Soft Computing, Volume 13, Issue 10, October 2013, Pages 4023–4037.

Chao-Yang Pang, Ben-Qiong Hu, Jie Zhang, Wei Hu, and Zheng-Chao Shan, “Applying Data Clustering Feature to Speed Up Ant Colony Optimization”, Hindawi Publishing Corporation, Abstract and Applied Analysis, Volume 2014, May 2014, Pages 1-9.

Xin Chen, Yongquan Zhou, and Qifang Luo, “A Hybrid Monkey Search Algorithm for Clustering Analysis”, Hindawi Publishing Corporation, The Scientific World Journal, Volume 2014, March 2014, Pages 1-17.

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

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

Yang Xu, Mingming Zeng, Quanhui Liu and Xiaofeng Wang, “A Genetic Algorithm Based Multilevel Association Rules Mining for Big Datasets”, Mathematical Problems in Engineering, Hindawi Publishing Corporation, Volume 2014 (2014), Article ID 867149, 9 pages

Yang Ou, Zheng Jiang Liu, Hamid Reza Karimi and Ying Tian, “Multilevel Association Rule Mining for Bridge Resource Management Based on Immune Genetic Algorithm”, Abstract and Applied Analysis, Hindawi Publishing Corporation, Volume 2014 (2014), Article ID 278694, 8 pages

N. Hoque, B. Nath and D. K. Bhattacharyya, “An Efficient Approach on Rare Association Rule Mining”, Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012), Springer, Volume 201 of the series Advances in Intelligent Systems and Computing, Pages 193-203, 2013

D. Vimal Kumar and A. Tamilarasi, “Genetic algorithm-based optimized association rule mining for multi-relational data”, Intelligent Data Analysis, Volume 17, Issue 6, Pages 965-980, November 2013

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.

Osvaldo Navarro, Rene´ Cumplido, Luis Villasen or-Pineda, Claudia Feregrino-Uribe, Jesu´ s Ariel Carrasco-Ochoa, “A Node Linkage Approach for Sequential Pattern Mining”, PLOS ONE, Volume 9, Issue 6, June 2014 Pages 1-16.

Sarra Senhadji, Salim Khiat, and Hafida Belbachir, “Association Rule Mining and Load Balancing Strategy in Grid Systems”, The International Arab Journal of Information Technology, Volume 11, Issue 4, July 2014, Pages 338-344.

Vimal Dhanasekar and Tamilarasi Angamuthu, “An Efficient Approach for Effectual Mining of Relational Patterns from Multi-Relational Database”.

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

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

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

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

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

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

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

Yang Xu, Mingming Zeng, Quanhui Liu, and Xiaofeng Wang, “A Genetic Algorithm Based Multilevel Association Rules Mining for Big Datasets”, Mathematical Problems in Engineering, Hindawi Publication Corporation, Volume 2014 (2014), Article ID 867149, 9 pages

Tamir Tassa, “Secure Mining of Association Rules in Horizontally Distributed Databases”, IEEE Transactions on Knowledge and Data Engineering, Volume: 26, Issue: 4, Pages 970 – 983, April 2014

Zhong-jie Zhang, Jian Huang, and Ying Wei, “FI-FG: Frequent Item Sets Mining from Datasets with High Number of Transactions by Granular Computing and Fuzzy Set Theory”, Hindawi Publishing Corporation, Mathematical Problems in Engineering, Volume 2015 (2015), Article ID 623240, 14 pages

Sample Dataset for Market Basket Analysis: http://weka.8497.n7.nabble.com/Sample-Dataset-for-Market-Basket-Analysis-td4973.html


Refbacks

  • There are currently no refbacks.


MAYFEB Journal of Electrical and Computer Engineering
MAYFEB TECHNOLOGY DEVELOPMENT
Toronto, Ontario, Canada