A fast clustering algorithm based on embedding technology for heterogeneous information networks

被引：0
|
作者：
Chen, Li-Min [1 ,2 ]
Yang, Jing [1 ]
Zhang, Jian-Pei [1 ]
机构：
[1] Institute of Computer Science and Technology, Harbin Engineering University, Harbin
[2] Institute of Computer Science and Technology, Mudanjiang Normal University, Mudanjiang
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2015年 / 37卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Clustering; Commute distance; Embedding; Heterogeneous information network; Sum of weighted distances;
D O I：
10.11999/JEIT150106
中图分类号：
学科分类号：
摘要：
Research on clustering heterogeneous information networks is one of the current hotspots. Taking advantages of the sparsity of heterogeneous information networks, a fast clustering algorithm based on embedding technology for heterogeneous information networks of star network schema is proposed in this paper. First, the heterogeneous information network is transformed into some compatible bipartite graphs from the point of compatible view. Then, the approximate commute distance embedding of each bipartite graph is computed via random mapping and a linear time solver, and an indicator subset in each embedding indicates the target dataset. At last, a general model is formulated via all the indicator subsets, and a minimum value of the model is derived by simultaneously clustering all of the indicator subsets using the sum of the weighted distances for all indicators for an identical target object. This proposed algorithm is effective by theory analysis and experimental verification. © 2015, Science Press. All right reserved.
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页码：2634 / 2641
页数：7
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