%0 Journal Article
%T 网络异构信息的张量分解聚类方法
%A 吴继冰
%A 黄宏斌
%A 邓苏&lt
%A /br&gt
%A WU Jibing
%A HUANG Hongbin
%A DENG Su
%J 国防科技大学学报
%D 2018
%X 提出基于张量分解的聚类算法，能够同时处理网络中多类型、多语义关系的异构信息。网络信息体系中的各种异构信息被建模为一个多维张量，异构信息之间丰富的语义关系建模为张量中的元素。提出有效的张量分解方法，将不同类型的信息对象一次性划分到不同的簇中。在人工合成的数据集和真实数据集上的实验结果表明：该聚类方法可以很好地处理网络信息体系中的异构信息聚类问题，并且性能优于现有的聚类方法。</br>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.
%K 聚类 异构信息 张量分解 信息网络&lt
%K /br&gt
%K clustering heterogeneous information tensor decomposition information networks
%U http://journal.nudt.edu.cn/gfkjdxxb/ch/reader/view_abstract.aspx?file_no=201805022&flag=1