Tensor decomposition based clustering method for heterogeneous information in networks

被引：0
|
作者：
Wu J. [1 ]
Huang H. [1 ]
Deng S. [1 ]
机构：
[1] Science and Technology on Information Systems Engineering Laboratory, College of Systems Engineering, National University of Defense Technology, Changsha
来源：
Deng, Su (sudeng@sohu.com) | 2018年 / National University of Defense Technology卷 / 40期
关键词：
Clustering; Heterogeneous information; Information networks; Tensor decomposition;
D O I：
10.11887/j.cn.201805022
中图分类号：
学科分类号：
摘要：
A tensor decomposition based clustering method was proposed for heterogeneous information in networks. This clustering method can cluster multiple types of objects and rich semantic relationships simultaneously. The multi-types of information objects in networks were modeled as a high-dimensional tensor, and the rich semantic relationships among different types of objects were modeled as elements in the tensor. Based on an effective tensor decomposition method, the multi-types of objects were partitioned into different clusters simultaneously. The experimental results on both synthetic datasets and real-world dataset show that the proposed clustering method can deal with the heterogeneous information in networks well, and can outperform the state-of-the-art clustering algorithms. © 2018, NUDT Press. All right reserved.
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页码：146 / 152and170
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