文献类型： 外文期刊

作者： Zhang, Bei ¹ ; Wang, Luquan ¹ ; Li, Yuanyuan ² ;

作者机构： 1.Shandong Xiandai Univ, Sch Econ & Management, Jinan 250104, Shandong, Peoples R China

2.Shandong Acad Agr Sci, Shandong Acad Grape, Jinan 250100, Shandong, Peoples R China

期刊名称：COMPLEXITY （ 影响因子：2.462； 五年影响因子：2.474 ）

ISSN： 1076-2787

年卷期： 2021 年 2021 卷

页码：

收录情况： SCI

摘要： 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.