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A Customer Segmentation Model Based on Affinity Propagation Algorithm and Improved Genetic K-Means Algorithm
Abstract : Customer Relationship Management System (CRM) has accumulated massive customer transaction data. Effective customer segmentation by analyzing transaction data can contribute to marketing strategy designing. However, the state-of-the-art researches are defective such as the uncertain number of clusters and the low accuracy. In this paper, a novel customer segmentation model, AP-GKAs, is proposed. First, factor analysis extracts customer feature based on multi-indicator RFM model. Then, affinity propagation (AP) determines the number of customer clusters. Finally, the improved genetic K-means algorithm (GKAs) is used to increase clustering accuracy. The experimental results showed that the AP-GKAs has higher segmentation performance in comparison to other typical methods.
Cited literature [10 references]
https://hal.inria.fr/hal-02197804
Contributor : Hal Ifip <>
Submitted on : Tuesday, July 30, 2019 - 5:02:29 PM
Last modification on : Wednesday, February 5, 2020 - 5:54:01 PM
Contributor : Hal Ifip <>
Submitted on : Tuesday, July 30, 2019 - 5:02:29 PM
Last modification on : Wednesday, February 5, 2020 - 5:54:01 PM
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473854_1_En_32_Chapter.pdf
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Distributed under a Creative Commons Attribution 4.0 International License