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2016 | OriginalPaper | Buchkapitel

Heterogeneous Information Networks Bi-clustering with Similarity Regularization

verfasst von : Xianchao Zhang, Haixin Li, Wenxin Liang, Linlin Zong, Xinyue Liu

Erschienen in: Intelligence and Security Informatics

Verlag: Springer International Publishing

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Abstract

Clustering analysis of multi-typed objects in heterogeneous information network (HINs) is an important and challenging problem. Nonnegative Matrix Tri-Factorization (NMTF) is a popular bi-clustering algorithm on document data and relational data. However, few algorithms utilize this method for clustering in HINs. In this paper, we propose a novel bi-clustering algorithm, BMFClus, for HIN based on NMTF. BMFClus not only simultaneously generates clusters for two types of objects but also takes rich heterogeneous information into account by using a similarity regularization. Experiments on both synthetic and real-world datasets demonstrate that BMFClus outperforms the state-of-the-art methods.

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Vorheriges Kapitel The Use of Reference Graphs in the Entity Resolution of Criminal Networks

Nächstes Kapitel A Profile-Based Authorship Attribution Approach to Forensic Identification in Chinese Online Messages

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Titel: Heterogeneous Information Networks Bi-clustering with Similarity Regularization
verfasst von: Xianchao Zhang
Haixin Li
Wenxin Liang
Linlin Zong
Xinyue Liu
Verlag: Springer International Publishing
Buch: Intelligence and Security Informatics
Print ISBN: 978-3-319-31862-2

Electronic ISBN: 978-3-319-31863-9

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-31863-9_2

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