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Association Rule - Extracting Knowledge Using Market Basket Analysis

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Author Affiliations

  • 1Department of computer science, Vivekanand College, Tarabai park Kolhapur, MH, INDIA
  • 2 Chh. Shahu Institute of business Education and Research Centre Kolhapur, MH, INDIA
  • 3 Department of the Computer Science, D.Y. Patil College of engineering, Kolhapur, MH, INDIA

Res. J. Recent Sci., Volume 1, Issue (2), Pages 19-27, February,2 (2012)

Abstract

Decision making and understanding the behavior of the customer has become vital and challenging problem for organizations to sustain their position in the competitive markets. Technological innovations have paved breakthrough in faster processing of queries and sub-second response time. Data mining tools have become surest weapon for analyzing huge amount of data and breakthrough in making correct decisions. The objective of this paper is to analyze the huge amount of data thereby exploiting the consumer behavior and make the correct decision leading to competitive edge over rivals. Experimental analysis has been done employing association rules using Market Basket Analysis to prove its worth over the conventional methodologies.

References

  1. Pillai Jyothi, User centric approach to itemset utility mining in Market Basket Analysis, International Journal on Computer Science and Engineering (IJCSE),3, (2011)
  2. Parvinder S. Sandhu Dalvinder, Dhaliwal S. and Panda S.N., Mining utility-oriented association rules: An efficient approach based on profit and quantity, International Journal of the Physical Sciences,6(2), 301-307 (2011)
  3. Vijaylakshmi S., Mohan V., Suresh Raja S., Mining of users access behavior for frequent sequential pattern from web logs, International Journal of Database Management System (IJDM),, (2010)
  4. Yldz B. and Ergenç B., (Turkey) in Comparison of Two Association Rule Mining Algorithms without Candidate Generation, International Journal of Computing and ICT Research, 674(131), 450-457 2010)
  5. Nan-chan Hsich, Kuo-Chang cha Enhancing consumer behavior analysis by data mining techniques (2009)
  6. Peter P. Wakabi-Waiswa Venansius Baryamureeba, Extraction Of Interesting Association Rules Using Genetic Algorithms International Journal of Computing and ICT Research, 2(1), (2008)
  7. Shrivastava A. and Sahu R., Efficient Association Rule Mining for Market Basket Analysis, Global Journal of e-Business and Knowledge Management, 3(1),(2007)
  8. Junzo Watada and Kozo Yamashiro, A Data Mining Approach to consumer behavior- Procedings of the first International Conference on Innovative computing Information(2006)
  9. Giudici Paulo. Applied Data mining :Statistical Methods for business and industry, -ISBN 9812-53-178-5 (2003)
  10. Jiauei Han and Michele Kamber, Data mining Concepts and Techniques, Simon Fraser University, ISBN 1-55860-489-8-(2001)
  11. Aggarwal C.C. and Yu P.S. Mining association with the collective strength approach, knowledge and data engg., IEEE,13(6) 863-873 (2001)
  12. Mike Chapple, Data mining an introduction Classification (2001)
  13. Lars parner -Consumer behavior – The psychology of marketing (2000)
  14. Sergey Brin, Rajeev Motwani and Craig silvertrin Beyound Market Baskets: Generalizing association rules for correlation, SIGMOD Record, (ACM Special Interest Group on Management of Data), 26(2), 265, (1997)
  15. S. Kotsiantis Association Rules mining (1994)
  16. Agrawal R. and Srikat, Fast Algorithms for Mining Association Rules- l. Sept (1994)
  17. Agrawal R., Imilienski T. and Swami A., Mining Associations Rules between Sets of Items in large databases. Proc. of the ALM SIGNOD. Int’l conf. on management of Data, 207-216 (1993)