1. Research on Segmenting E-Commerce Customer through an Improved K-Medoids Clustering Algorithm
- Author
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Zengyuan Wu, Lingmin Jin, Jiali Zhao, Lizheng Jing, and Liang Chen
- Subjects
ComputingMethodologies_PATTERNRECOGNITION ,General Computer Science ,Article Subject ,General Mathematics ,General Neuroscience ,Commerce ,Cluster Analysis ,General Medicine ,Algorithms - Abstract
In view of the shortcomings of traditional clustering algorithms in feature selection and clustering effect, an improved Recency, Frequency, and Money (RFM) model is introduced, and an improved K-medoids algorithm is proposed. Above model and algorithm are employed to segment customers of e-commerce. First, traditional RFM model is improved by adding two features of customer consumption behavior. Second, in order to overcome the defect of setting K value artificially in traditional K-medoids algorithm, the Calinski–Harabasz (CH) index is introduced to determine the optimal number of clustering. Meanwhile, K-medoids algorithm is optimized by changing the selection of centroids to avoid the influence of noise and isolated points. Finally, empirical research is done using a dataset from an e-commerce platform. The results show that our improved K-medoids algorithm can improve the efficiency and accuracy of e-commerce customer segmentation.
- Published
- 2022