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Customer Clustering Using a Combination of Fuzzy C-Means and Genetic Algorithms

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  • Azarnoush Ansari
  • Arash Riasi

Abstract

This study intends to combine the fuzzy c-means clustering and genetic algorithms to cluster the customers of steel industry. The customers were divided into two clusters by using the variables of the LRFM (length, recency, frequency, monetary value) model. Results indicated that customers belonging to the first cluster had a higher length of the relationship, recency of trade, and frequency of trade but lower monetary value compared to the average values of these criteria for all customers. The results also showed that customers belonging to the secondcluster had a higher recency of trade and monetary value but lower length of the relationship and frequency of trade compared to the average values of these criteria for all customers. It was also found that the combined algorithm (i.e., fuzzy c-means clustering and genetic algorithm) used in this study had a lower mean squared error (MSE) compared to fuzzy c-means clustering.

Suggested Citation

  • Azarnoush Ansari & Arash Riasi, 2016. "Customer Clustering Using a Combination of Fuzzy C-Means and Genetic Algorithms," International Journal of Business and Management, Canadian Center of Science and Education, vol. 11(7), pages 1-59, June.
    • Handle: RePEc:ibn:ijbmjn:v:11:y:2016:i:7:p:59
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    References listed on IDEAS

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    1. Coussement, Kristof & Van den Bossche, Filip A.M. & De Bock, Koen W., 2014. "Data accuracy's impact on segmentation performance: Benchmarking RFM analysis, logistic regression, and decision trees," Journal of Business Research, Elsevier, vol. 67(1), pages 2751-2758.
    2. Shouhong Wang, 2001. "Cluster analysis using a validated self‐organizing method: cases of problem identification," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(2), pages 127-138, June.
    3. McCarty, John A. & Hastak, Manoj, 2007. "Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression," Journal of Business Research, Elsevier, vol. 60(6), pages 656-662, June.
    4. Kim, Kyung Hoon & Kim, Kang Sik & Kim, Dong Yul & Kim, Jong Ho & Kang, Suk Hou, 2008. "Brand equity in hospital marketing," Journal of Business Research, Elsevier, vol. 61(1), pages 75-82, January.
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    Cited by:

    1. Oghuvwu, M. E. & Omoye, A.S, 2016. "Mergers, Acquisitions and Corporate Performance: The Balanced Scorecard Approach," Accounting and Finance Research, Sciedu Press, vol. 5(4), pages 1-63, November.
    2. Shengkun Xie & Chong Gan, 2023. "Estimating Territory Risk Relativity Using Generalized Linear Mixed Models and Fuzzy C -Means Clustering," Risks, MDPI, vol. 11(6), pages 1-20, May.
    3. Tristan Bester & Benjamin Rosman, 2024. "Towards Financially Inclusive Credit Products Through Financial Time Series Clustering," Papers 2402.11066, arXiv.org.
    4. Chitra Gunshekhar Gounder & M. Venkateshwarlu, 2017. "Shareholder Value Creation: An Empirical Analysis of Indian Banking Sector," Accounting and Finance Research, Sciedu Press, vol. 6(1), pages 148-148, February.

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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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