基于动态加权张量距离的多聚类算法  

Multiple clustering algorithm based on dynamic weighted tensor distance

作　　者：薛状状 李鹏 樊卫北[1,2] 张宏俊 孟凡朔 XUE Zhuangzhuang;LI Peng;FAN Weibei;ZHANG Hongjun;MENG Fanshuo(School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing Jiangsu 210023,China;Institute of Network Security and Trusted Computing,Nanjing University of Posts and Telecommunications,Nanjing Jiangsu 210023,China)

机构地区：[1]南京邮电大学计算机学院、软件学院、网络空间安全学院,南京210023 [2]南京邮电大学网络安全与可信计算研究所,南京210023

出　　处：《计算机应用》2023年第11期3449-3456,共8页journal of Computer Applications

基　　金：国家自然科学基金资助项目(62102194);江苏省“六大人才高峰”高层次人才项目(RJFW-111)。

摘　　要：基于张量的多聚类算法(TMC)在衡量属性重要性时忽略了对象张量内部属性组合的关联性,而且在不同的特征空间选择下,固定权重策略导致所选与未选择特征空间没有完全分离。针对上述问题,提出一种基于动态加权张量距离(DWTD)的多聚类算法(DWTD-MC)。首先,为提升各特征空间属性重要性衡量的准确性,建立了自-关联张量模型;其次,构建多视图权重张量模型,在不同特征空间选择下通过动态加权策略满足多聚类分析的需求;最后,使用DWTD衡量数据点的相似性,生成最终的多聚类结果。在真实数据集上的仿真实验结果表明,DWTD-MC在杰卡德指数(JI)、邓恩指数(DI)、DB指数(DB)和轮廓系数(SC)评价指标上均优于TMC等对比算法,而且可以在获得较高质量的聚类结果的同时,使各聚类结果之间保持较低的冗余度,满足多聚类分析的任务需求。When measuring the importance of attributes in Tensor-based Multiple Clustering algorithm(TMC),the relevance of attribute combinations within object tensors are ignored,and the selected and unselected feature space are incompletely separated because of the fixed weight strategy under different feature space selection.For above problems,a Multiple Clustering algorithm based on Dynamic Weighted Tensor Distance(DWTD-MC)was proposed.Firstly,a selfassociation tensor model was constructed to improve the accuracy of attribute importance measurement of each feature space.Then,a multi-view weight tensor model was built to meet the task requirements of multiple clustering analysis by dynamic weighting strategy under different feature space selection.Finally,the dynamic weighted tensor distance was used to measure the similarity of data points,generating multiple clustering results.Simulation results on real datasets show that DWTD-MC outperforms comparative algorithms such as TMC in terms of Jaccard Index(JI),Dunn Index(DI),Davies-Bouldin index(DB)and Silhouette Coefficient(SC).It can obtain high quality clustering results while maintaining low redundancy among clustering results,as well as meeting the task requirements of multiple clustering analysis.

关 键 词：异构数据 多聚类 张量 张量距离 动态加权 社会物理信息系统 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]