In user cluster analysis, users with the same or similar behavior characteristics are divided into the same group by iterative update clustering, and the core and larger user groups are detected. In this paper, we present the formulation and data mining of the correlation rules based on the clustering algorithm through the definition and procedure of the algorithm. In addition, based on the idea of the K-mode clustering algorithm, this paper proposes a clustering method combining related rules with multivalued discrete features (MDF). In this paper, we construct a method to calculate the similarity between users using Jaccard distance and combine correlation rules with Jaccard distances to improve the similarity between users. Next, we propose a clustering method suitable for MDF. Finally, the basic K-mode algorithm is improved by the similarity measure method combining the correlation rule with the Jaccard distance and the cluster center update method which is the ARMDKM algorithm proposed in this paper. This method solves the problem that the MDF cannot be effectively processed in the traditional model and demonstrates its theoretical correctness. This experiment verifies the correctness of the new method by clustering purity, entropy, contour, and other indicators.

中文翻译：

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基于聚类算法的电子商务平台精准营销方法

在用户集群分析中，具有相同或相似行为特征的用户通过迭代更新聚类划分为同一组，并检测核心用户组和较大的用户组。本文通过聚类算法的定义和过程，提出了基于聚类算法的相关规则的表述和数据挖掘。此外，基于K模式聚类算法的思想，提出了一种将相关规则与多值离散特征（MDF）相结合的聚类方法。在本文中，我们构造了一种使用Jaccard距离来计算用户之间相似度的方法，并将相关规则与Jaccard距离相结合以提高用户之间的相似度。接下来，我们提出一种适用于MDF的聚类方法。最后，基本通过将相关规则与Jaccard距离相结合的相似性度量方法和本文提出的ARMDKM算法聚类中心更新方法，对K模式算法进行了改进。该方法解决了传统模型无法有效处理中密度纤维板的问题，并证明了其理论正确性。该实验通过对纯度，熵，轮廓和其他指标进行聚类验证了新方法的正确性。