典型文献
Nonnegative Matrix Tri-Factorization Based Clustering in a Heterogeneous Information Network with Star Network Schema
文献摘要：
Heterogeneous Information Networks(HINs)contain multiple types of nodes and edges;therefore,they can preserve the semantic information and structure information.Cluster analysis using an HIN has obvious advantages over a transformation into a homogenous information network,which can promote the clustering results of different types of nodes.In our study,we applied a Nonnegative Matrix Tri-Factorization(NMTF)in a cluster analysis of multiple metapaths in HIN.Unlike the parameter estimation method of the probability distribution in previous studies,NMTF can obtain several dependent latent variables simultaneously,and each latent variable in NMTF is associated with the cluster of the corresponding node in the HIN.The method is suited to co-clustering leveraging multiple metapaths in HIN,because NMTF is employed for multiple nonnegative matrix factorizations simultaneously in our study.Experimental results on the real dataset show that the validity and correctness of our method,and the clustering result are better than that of the existing similar clustering algorithm.
文献关键词：
中图分类号：
作者姓名：
Juncheng Hu;Yongheng Xing;Mo Han;Feng Wang;Kuo Zhao;Xilong Che
作者机构：
College of Computer Science and Technology,Jilin University,Changchun 130012,China;School of Intelligent Systems Science and Engineering,Jinan University,Zhuhai 519070,China
文献出处：
引用格式：
[1]Juncheng Hu;Yongheng Xing;Mo Han;Feng Wang;Kuo Zhao;Xilong Che-.Nonnegative Matrix Tri-Factorization Based Clustering in a Heterogeneous Information Network with Star Network Schema)[J].清华大学学报自然科学版（英文版）,2022(02):386-395
A类：
HINs,NMTF,metapaths,factorizations
B类：
Nonnegative,Matrix,Tri,Factorization,Based,Clustering,Heterogeneous,Information,Star,Schema,Networks,contain,multiple,types,nodes,edges,therefore,they,can,preserve,semantic,information,structure,analysis,using,has,obvious,advantages,over,transformation,into,homogenous,network,which,promote,clustering,results,different,our,study,we,applied,Unlike,parameter,estimation,method,probability,distribution,previous,studies,obtain,several,dependent,latent,variables,simultaneously,each,associated,corresponding,suited,leveraging,because,employed,nonnegative,matrix,Experimental,real,dataset,show,that,validity,correctness,are,better,than,existing,similar,algorithm
AB值：
0.494717
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