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Research on Segmenting E-Commerce Customer through an Improved K-Medoids Clustering Algorithm.
- Source :
-
Computational intelligence and neuroscience [Comput Intell Neurosci] 2022 Jun 18; Vol. 2022, pp. 9930613. Date of Electronic Publication: 2022 Jun 18 (Print Publication: 2022). - Publication Year :
- 2022
-
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.<br />Competing Interests: The authors declare that they have no conflicts of interest.<br /> (Copyright © 2022 Zengyuan Wu et al.)
- Subjects :
- Cluster Analysis
Algorithms
Commerce
Subjects
Details
- Language :
- English
- ISSN :
- 1687-5273
- Volume :
- 2022
- Database :
- MEDLINE
- Journal :
- Computational intelligence and neuroscience
- Publication Type :
- Academic Journal
- Accession number :
- 35761867
- Full Text :
- https://doi.org/10.1155/2022/9930613