Data characterization using visualization based on Customer Buying Pattern by Association Rule Mining Algorithms

Authors

  • U. Suba  MCA Student Department of Computer Applications,Anna University ,BIT Campus,Tiruchirappalli, India
  • R. Karthiyayini  Assistant professor, Department of Computer Applications, Anna University, BIT Campus, Tiruchirappalli, India

Keywords:

Business Intelligence ,Hidden Pattern,Market Based Analysis, Apriori Algorithm ,All Electronic Data Repository

Abstract

Association rule mining is a data mining technique which consists of variety of algorithms to identify the relationships between the data set. Specifically Frequent pattern mining is a technique to find the association between the data set in any discipline. Using this technique the data miner can extract any kinds of hidden patterns in order to promote their discipline such as business intelligence, medical analysis, and scientific environment etc,. This system focused on business intelligence data where market basket analysis are performed by the Apriori algorithm already. In this system the data sets are constructed by the provision of AllElectronics data repository. The main goal of this system is for effective data summarization and characterization using visualization techniques.

References

  1. Weng, S.-S., Liu, J.-L.: Feature-based recommendations for one-to-one marketing, Expert Systems with Applications, Vol. 26, 2004, pp. 493-508.
  2. Chen, Y.-L., Tang, K., Shen, R.-J., Hu, Y.-H.: Market basket analysis in a multiple store environment, Decision Support Systems, 2004.
  3. Berry, M.J.A., Linoff, G.S.: Data Mining Techniques: for Marketing, Sales and Customer Relationship Management(second edition), Hungry Minds Inc., 2004.
  4. Agrawal R, Srikant R, Fast algorithms for mining association rules. In: Proceedings of the 20th VLDB conference,1994, pp 487–499.
  5. J. Han, H. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation. In: Proc. Conf. on the Management of Data SIGMOD’00, ACM Press, New York, NY, USA 2000.
  6. J. Han and M. Kamber. Data Mining: Concepts and Techniuqes, Morgan Kaufmann Publishers, San Francisco, CA, 2001.
  7. (IJACSA) International Journal of Advanced Computer Scienc and Applications, Vol. 7, No. 3, 2016 .Jamal Alsakran .
  8. Montaner M., Lopez B., and Josep R., A Taxonomy of Recommender Agents on the Internet, Artificial Intelligence Review, 19, 285-330, 2003.
  9. International journal of business and management.(www.ccsenet.org/journal.html) .The research is supported by MOE Project of Humanities and Social Science in Chinese University (O8JC870011).
  10. CEO Pay and Appointments: A Market-Based Explanation for Recent Trends By KEVIN J. MURPHY AND JAN ZABOJNIK

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-04-30

Issue

Volume 3, Issue 5, May-June-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
U. Suba, R. Karthiyayini, " Data characterization using visualization based on Customer Buying Pattern by Association Rule Mining Algorithms " International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.416-422, May-June-2018.