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Multi-type clustering in heterogeneous information networks

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Abstract

Heterogeneous information networks have drawn much attention in recent years due to their significant applications, such as text mining, e-commerce, social networks, and bioinformatics. Clustering different types of objects simultaneously based upon not only their relations of the same type, but also the relations between different types of objects can improve the clustering quality mutually. In this paper, we propose a general model, in which both the homogeneous and heterogeneous relations are considered simultaneously, to describe the structure of the heterogeneous information networks and devise a novel parametric free multi-type overlapped clustering approach. In this model, different types of relations between different types of objects are represented by a group of matrices. In this way, we transfer the multi-type clustering problem into the information compression problem. Subsequently, greedy search approaches, which aim at describing the group of relational matrices with least bits, are proposed. Moreover, by discovering the discriminative clusters among different types of objects, we devise effective parameter-free strategies to discover either overlapping or non-overlapping structure among different types of clusters. Extensive experiments on real-world and synthetic data sets demonstrate our methods are effective and efficient.

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Notes

  1. http://www.informatik.uni-trier.de/ley/db/.

  2. All logarithms are based on 2 in this paper.

  3. http://www.dataminingresearch.com/index.php/2010/09/classic3-classic4-datasets.

  4. http://people.csail.mit.edu/jrennie/20Newsgroups/.

  5. http://www-users.cs.umn.edu/han/data/.

  6. http://mlg.ucd.ie/datasets.

  7. http://www.cs.uiuc.edu/homes/sun22/data/.

  8. http://arnetminer.org/.

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Acknowledgments

Wangqun Lin and Bo Deng are supported by National Natural Science Foundation of China through Grant 61271252. Philip S. Yu and Yuchen Zhao are supported by NSF through Grant CNS-1115234, Google Research Award, and the Pinnacle Lab at Singapore Management University.

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Authors and Affiliations

  1. Beijing Institute of System Engineering, Beijing, China

    Wangqun Lin & Bo Deng

  2. University of Illinois at Chicago, Chicago, IL, USA

    Philip S. Yu & Yuchen Zhao

  3. Institute of Data Science, Tsinghua University, Beijing, China

    Philip S. Yu

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  1. Wangqun Lin
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  4. Bo Deng
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Correspondence to Wangqun Lin.

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Lin, W., Yu, P.S., Zhao, Y. et al. Multi-type clustering in heterogeneous information networks. Knowl Inf Syst 48, 143–178 (2016). https://doi.org/10.1007/s10115-015-0869-9

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