College of Economics and Management, China Jiliang University, No. 258, Xueyuan Street, Hangzhou, Zhejiang 310018, China.
College of Optical and Electronic Technology, China Jiliang University, No. 258, Xueyuan Street, Hangzhou, Zhejiang 310018, China.
Comput Intell Neurosci. 2022 Jun 18;2022:9930613. doi: 10.1155/2022/9930613. eCollection 2022.
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.
针对传统聚类算法在特征选择和聚类效果方面的不足，引入了改进的最近一次、频率和货币价值（RFM）模型，并提出了一种改进的 K 均值算法。利用上述模型和算法对电子商务客户进行细分。首先，通过添加客户消费行为的两个特征对传统 RFM 模型进行改进。其次，为了克服传统 K 均值算法中人为设置 K 值的缺陷，引入了 Calinski-Harabasz（CH）指数来确定最佳聚类数量。同时，通过改变质心的选择来优化 K 均值算法，以避免噪声和孤立点的影响。最后，使用电子商务平台上的数据集进行实证研究。结果表明，改进的 K 均值算法可以提高电子商务客户细分的效率和准确性。
