基于k多数值代表的混合矩阵对象数据聚类  

Mixed Matrix Object Data Clustering Based on k-Multiple Value Representation

作　　者：赵健[1] ZHAO Jian(Department of Computer,Changzhi University,Changzhi 046011,Shanxi Province,China)

机构地区：[1]长治学院计算机系,山西长治046011

出　　处：《吉林大学学报（理学版）》2022年第4期929-942,共14页Journal of Jilin University:Science Edition

基　　金：国家自然科学基金(批准号:61602061);山西省教育科学“十三五”规划项目(批准号:GH-18098).

摘　　要：针对数据的稀疏性和高维问题,提出一种基于k多数值代表的混合矩阵对象数据聚类方法,有效反映聚类中心与聚类内矩阵对象的分布.为充分挖掘数据之间的隐含信息,该算法首先定义两个数值矩阵对象之间的相异度度量,给出一种更新聚类中心的启发式方法;然后进一步提出一种基于k多数值代表的混合矩阵对象数据聚类算法;最后在真实数据集与合成数据集上进行仿真实验.实验结果表明,该算法能有效实现包含大量记录、矩阵对象、属性的数据聚类.Aiming at the problems of data sparsity and high dimension,the author proposed a mixed matrix object data clustering method based on k-multiple value representation,which effectively reflected the distribution of cluster centers and matrix objects in clusters.In order to fully mine the hidden information between data,firstly,the algorithm defined the dissimilarity measure between two numerical matrix objects,and gave a heuristic method to update the clustering center.Secondly,a mixed matrix object data clustering algorithm based on k-multiple value representation was proposed.Finally,the simulation experiments were carried out on real data sets and composite data sets.The experimental results show that the proposed algorithm can effectively realize data clustering including a large number of records,matrix objects and attributes.

关 键 词：多数值代表 聚类 稀疏性 矩阵对象 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]