In recent years, data mining technologies have been well applied to many domains, including e-commerce. In customer relationship management (CRM), the RFM analysis model is one of the most effective approaches to increase the profits of major enterprises. However, with the rapid development of e-commerce, the diversity and abundance of e-commerce data pose a challenge to mining efficiency. Moreover, in actual market transactions, the chronological order of transactions reflects customer behavior and preferences. To address these challenges, we develop an effective algorithm called SeqRFM, which combines sequential pattern mining with RFM models. SeqRFM considers each customer's recency (R), frequency (F), and monetary (M) scores to represent the significance of the customer and identifies sequences with high recency, high frequency, and high monetary value. A series of experiments demonstrate the superiority and effectiveness of the SeqRFM algorithm compared to the most advanced RFM algorithms based on sequential pattern mining. The source code and datasets are available at GitHub https://github.com/DSI-Lab1/SeqRFM.

翻译：近年来，数据挖掘技术已广泛应用于电子商务等多个领域。在客户关系管理（CRM）中，RFM分析模型是提升大型企业盈利能力的最有效方法之一。然而，随着电子商务的快速发展，电商数据的多样性和海量性对挖掘效率提出了挑战。此外，在实际市场交易中，交易的时间顺序反映了客户的行为和偏好。为应对这些挑战，我们开发了一种名为SeqRFM的有效算法，该算法将序列模式挖掘与RFM模型相结合。SeqRFM通过每位客户的最近性（R）、频率（F）和消费金额（M）得分来表征客户的重要性，并识别具有高最近性、高频率和高消费金额的序列。一系列实验表明，与基于序列模式挖掘的最先进RFM算法相比，SeqRFM算法具有优越性和有效性。源代码和数据集可在GitHub https://github.com/DSI-Lab1/SeqRFM 获取。