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A Novel Approach for the Customer Segmentation Using Clustering Through Self-Organizing Map

A Novel Approach for the Customer Segmentation Using Clustering Through Self-Organizing Map

Debaditya Barman, Nirmalya Chowdhury
Copyright: © 2019 |Volume: 6 |Issue: 2 |Pages: 23
ISSN: 2334-4547|EISSN: 2334-4555|EISBN13: 9781522568315|DOI: 10.4018/IJBAN.2019040102
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MLA

Barman, Debaditya, and Nirmalya Chowdhury. "A Novel Approach for the Customer Segmentation Using Clustering Through Self-Organizing Map." IJBAN vol.6, no.2 2019: pp.23-45. http://doi.org/10.4018/IJBAN.2019040102

APA

Barman, D. & Chowdhury, N. (2019). A Novel Approach for the Customer Segmentation Using Clustering Through Self-Organizing Map. International Journal of Business Analytics (IJBAN), 6(2), 23-45. http://doi.org/10.4018/IJBAN.2019040102

Chicago

Barman, Debaditya, and Nirmalya Chowdhury. "A Novel Approach for the Customer Segmentation Using Clustering Through Self-Organizing Map," International Journal of Business Analytics (IJBAN) 6, no.2: 23-45. http://doi.org/10.4018/IJBAN.2019040102

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Abstract

Customer segmentation is the process of forming smaller groups of customers according to their characteristics. Now companies can develop proper marketing strategies for each group to get the desired results. This type of direct marketing is practiced by most organizations from the size of smallest start-up to the Fortune 500 leaders. Clustering is the ideal data mining technique for customer segmentation. In this article, the authors have proposed a clustering algorithm based on the self-organizing map and minimum spanning tree for customer segmentation. The authors have used several synthetic and real-life datasets to evaluate the clustering performance of their approach. To demonstrate the effectiveness of the authors' proposed approach, they have trained few classifiers with the groups extracted from a direct marketing campaign of a Portuguese banking institution and show that the classification accuracy is better compared to the results obtained in some previous work where the full dataset has been used to train the same classifiers.

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