Original Research Article

Article volume = 2023 and issue = 1

Pages: 96–102

Article publication Date: November 20, 2023

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Comparative Study of Novel and Existing Fuzzy Clustering Algorithms for Customer Segmentation Based on a New RFM Model

Mahsa Hamidi and Omid Solaymni Fard

School of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran.


Abstract:

Customer segmentation has been a hot topic for decades, and the competition among businesses makes it more challenging.The main objective of this paper is to compare the performance of a novel fuzzy clustering algorithm with three other existing fuzzy-based clustering algorithms on a new RFM-based model for customer segmentation. The RFM model is a technique that uses four parameters, namely recency, frequency, monetary value, and duration, to analyze customer behavior and assign them to different clusters. The paper also aims to determine the optimal number of clusters for each algorithm by using internal evaluation measures, such as the silhouette coefficient,Davies Bouldin and Calinski Harabasz. You can find the data set and codes in <a href=https://github.com/mahsahamidi>my GitHub</a>.

Keywords:

Fuzzy clustering,customer segmentation, Validation index


References:
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Cite this article as:
  • Mahsa Hamidi and Omid Solaymni Fard, Comparative Study of Novel and Existing Fuzzy Clustering Algorithms for Customer Segmentation Based on a New RFM Model, Communications in Combinatorics, Cryptography & Computer Science, 2023(1), PP.96–102, 2023
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