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A customer segmentation model based on affinity propagation algorithm and improved genetic k-means algorithm
Zhang, Meiyang1; Zhang, Zili1,2; Qiu, Shi3
Conference Name10th IFIP International Conference on Intelligent Information Processing, IIP 2018
Source PublicationIntelligent Information Processing IX - 10th IFIP TC 12 International Conference, IIP 2018, Proceedings
Conference Date2018-10-19
Conference PlaceNanning, China
PublisherSpringer New York LLC
Contribution Rank3
AbstractCustomer 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. © IFIP International Federation for Information Processing 2018.
Indexed ByEI
EI Accession Number20184406019609
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Document Type会议论文
Corresponding AuthorZhang, Zili
Affiliation1.College of Computer and Information Science, Southwest University, Chongqing; 400715, China;
2.School of Information Technology, Deakin University, Locked Bag 20000, Geelong; VIC; 3220, Australia;
3.Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an; 710119, China
Recommended Citation
GB/T 7714
Zhang, Meiyang,Zhang, Zili,Qiu, Shi. A customer segmentation model based on affinity propagation algorithm and improved genetic k-means algorithm[C]:Springer New York LLC,2018:321-327.
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