Automatic Fuzzy Classification Tool for Customer Loyalty using Gaussian Membership Function
Retaining customers in current era is of utmost importance and crucial for the success of any business enterprise. Identifying potential and loyal customers for any enterprise is of great importance. Customer loyalty is one of the major aspects of Customer Relationship Management (CRM). It is very important for an organization to identify its loyal customers, so that the organization can provide better and special services to these customers in order to enhance their business. In this paper, the construction of fuzzy decision tree has been refined by assigning the weights to each attribute, as all the attributes may not affect the customer loyalty equally.
This paper proposes an application, which makes use of fuzzy decision trees for classifying customers into various categories. Rather than identifying a customer crisply falling near to the end boundaries, soft boundaries (concept of fuzziness) are used which classifies the customer into different predefined classes of loyalty from the information stored about the customers by the organization.
This procedure helps in classifying customers in a better way as compared to crisp classification. The proposed framework is evaluated on a sample dataset that provides encouraging results.
Arya and P. Aggarwal “Fuzzy Decision Tree based Automatic Classifie for Customer Loyalty”.In Proc. Of Intl. Conf. on Data Management , March 2010, pp.288-291 .
L. Yan-Li , H. Jing-Yuan, L. Fa-Chao and L. Shu-Shan,” Fuzzy synthetic evaluation on customer loyalty based on analytic hierarchy process”,In Proc. Of 4th Intl. Conf. on Machine learning and Cybernetics(‘05) pp. 2706- 2710.
Z. Qiaohong and L. Wenfeng,“The Research of Customer Classification Based on Extended Bayes Model”(IEEE)(‘08) pp. 22-25.
Y. Wei-Kun, Z. Mei-Hua and M. Jian,”Value-based Customer Loyalty Evolution”,(IEEE)(‘07) pp 1-5.
V. Kumar, T. Pang-Ning, S. Michael, “Introduction to Data Mining”, Pearson Education ,2009 ch.4, ch.5.
J. Han, M. Kamber, “Data Mining: Concepts and Techniques”, Morgan Kauffmann Publishers, 2006.
D. So yeung, J. Sun, W. Xi-Zhao, “An initial comparison of generalization-capability between crisp and fuzzy decision trees”, In Proc. Of Intl. Conf. on Machine Learning and cybernatics, Beijing, Nov 2002 pp 2008 - 2013.
Olaru, L. Wechenkel, “A Complete fuzzy decision tree technique”, In Intl. J. of Fuzzy sets and systems, Elsevier, 2003 pp 563-565.
J. B. Simha and S.S. Iyenger, “Fuzzy data mining for customer loyalty analysis”, In Proc. Of 9th Intl. Conf. (IEEE) on Information Technology (ICIT’06) pp 245-246.
J. Yen, R. Langari “Fuzzy logic” Intelligence, Control and Information”, Pearson Education ch.3.
Y. Peng and P. Flach., “Soft Discretization to Enhance the Continuous Decision Tree Induction”, Integrating Aspects of data Mining, Decision Support and Meta-Learning, Christophe Giraud-Carrier, Nada Lavrac and Steve Moyle, editors, pages 109-118, ECML/PKDD’ 01 workshop notes, Spetember 2001.
J. Ranilla, O. Luaces, and A. Bahamonde, “A Heuristic for Learning Decision Trees and Pruning them into Classification Rules,” Artificial Intelligence Comm., vol. 16, no. 2,2003, pp.71-87.
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.