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- W2893801087 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." @default.
- W2893801087 created "2018-10-05" @default.
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- W2893801087 date "2018-01-01" @default.
- W2893801087 modified "2023-10-07" @default.
- W2893801087 title "A Customer Segmentation Model Based on Affinity Propagation Algorithm and Improved Genetic K-Means Algorithm" @default.
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- W2893801087 doi "https://doi.org/10.1007/978-3-030-00828-4_32" @default.
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