Association Rule Mining Approach for Customer Relationship Management

Authors

  • Tamanna Kachwala  Ph. D. Scholar, Rai University, Saroda, Ahmedabad, Gujarat, India
  • L. K. Sharma  National Institute of Occupational Health, Ahmedabad, Gujarat, India

Keywords:

Customer relationship management, Association Rule Mining, Metrics in Association, Apriori

Abstract

The customer relationship management is the main goal of every business organization. In this competitive business scenario, every activity starts and ends with the customer. The increasing competition and dynamic environment, every firms needs to identify, anticipate and satisfy consumers to maximize profit. The association rule mining algorithms are using to work out data mining problem in a trendy way. Along with the large variety of existing approaches, it is constantly challenging to select the best possible algorithm for rule based mining task. Generally, empirical methods for evaluating algorithms of association rule mining are based completely on quantitative measures such as correlation between minimum support, a number of rules or frequent item sets and data processing time. In this paper, we are evaluating best rules found by applying the Apriori algorithm of association rule sets. We show that observing rule overlapping, support and confidence in two compared rule sets help evaluate algorithm quality or the measure uniformity of source datasets.

References

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

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Published

2018-04-30

Issue

Volume 3, Issue 3, March-April-2018

Section

Research Articles

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

How to Cite

[1]
Tamanna Kachwala, L. K. Sharma, " Association Rule Mining Approach for Customer Relationship Management, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 3, pp.1991-1995, March-April-2018.