Publication Type

Conference Proceeding Article

Publication Date

4-2014

Abstract

Discovering social communities of web users through clustering analysis of heterogeneous link associations has drawn much attention. However, existing approaches typically require the number of clusters a prior, do not address the weighting problem for fusing heterogeneous types of links and have a heavy computational cost. In this paper, we explore the feasibility of a newly proposed heterogeneous data clustering algorithm, called Generalized Heterogeneous Fusion Adaptive Resonance Theory (GHF-ART), for discovering communities in heterogeneous social networks. Different from existing algorithms, GHF-ART performs real-time matching of patterns and one-pass learning which guarantee its low computational cost. With a vigilance parameter to restrain the intra-cluster similarity, GHF-ART does not need the number of clusters a prior. To achieve a better fusion of multiple types of links, GHF-ART employs a weighting function to incrementally assess the importance of all the feature channels. Extensive experiments have been conducted to analyze the performance of GHF-ART on two heterogeneous social network data sets and the promising results comparing with existing methods demonstrate the effectiveness and efficiency of GHF-ART.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

Proceedings of the 14th SIAM International Conference on Data Mining (SDM 2014),Philadelphia, USA, Apr 24-26

Volume

2

First Page

803

Last Page

811

Identifier

10.1137/1.9781611973440.92

Publisher

SIAM

City or Country

US

Additional URL

https://doi.org/10.1137/1.9781611973440.92

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