A Fast Clustering Algorithm Based on Embedding Technology for Heterogeneous Information Networks

Chen Li-min; Yang Jing; Zhang Jian-pei

doi:10.11999/JEIT150106

Volume 37 Issue 11

Nov. 2015

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Chen Li-min, Yang Jing, Zhang Jian-pei. A Fast Clustering Algorithm Based on Embedding Technology for Heterogeneous Information Networks[J]. Journal of Electronics & Information Technology, 2015, 37(11): 2634-2641. doi: 10.11999/JEIT150106

Citation:

Chen Li-min, Yang Jing, Zhang Jian-pei. A Fast Clustering Algorithm Based on Embedding Technology for Heterogeneous Information Networks[J]. Journal of Electronics & Information Technology, 2015, 37(11): 2634-2641. doi: 10.11999/JEIT150106

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A Fast Clustering Algorithm Based on Embedding Technology for Heterogeneous Information Networks

doi: 10.11999/JEIT150106

1.
2.
(Institute of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China)

Funds:

The National Natural Science Foundation of China (61370083, 61073043, 61073041)

Received Date: 2015-01-21
Rev Recd Date: 2015-07-16
Publish Date: 2015-11-19

Abstract

Abstract

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.
- Heterogeneous information network,
- Clustering,
- Commute distance,
- Embedding,
- Sum of weighted distances

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References(24)

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