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Topic-Level Clustering on Web Resources

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Emerging Technologies for Education (SETE 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10108))

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Abstract

The rapid development of Internet, social media, and news portals has provided a large amount of information in various aspects. Confronting such plenty of resources, it is valuable to develop effective clustering approaches. However, performance of traditional clustering models on web resources is not good enough due to the high dimension. In this paper, we propose a clustering model based on topic model and density peaks. Our model combines biterm topic model and clustering by fast search of density peaks, which firstly extract a set of features with the co-occurrence of two words from the original documents, followed by clustering analysis via topical features. Web resources are translated from raw data into clusters, and evaluation on clustering results of center part verifies the effectiveness of the proposed method.

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Acknowledgements

The research work described in this article was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E06/14).

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Authors and Affiliations

  1. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China

    Shiyu Zhao

  2. Caritas Institute of Higher Education, Tseung Kwan O, Hong Kong

    Fu Lee Wang & Leung Pun Wong

Authors
  1. Shiyu Zhao
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  2. Fu Lee Wang
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  3. Leung Pun Wong
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Corresponding author

Correspondence to Fu Lee Wang .

Editor information

Editors and Affiliations

  1. National Yunlin University of Science and Technology, Yunlin, Taiwan

    Ting-Ting Wu

  2. Free University of Bozen-Bolzano, Rome, Italy

    Rosella Gennari

  3. National Cheng-Kung University, Tainan, Taiwan

    Yueh-Min Huang

  4. The Education University of Hong Kong, Hong Kong, Hong Kong

    Haoran Xie

  5. MC Information Multimedia Communication AG, Saarbrücken, Germany

    Yiwei Cao

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Zhao, S., Wang, F.L., Wong, L.P. (2017). Topic-Level Clustering on Web Resources. In: Wu, TT., Gennari, R., Huang, YM., Xie, H., Cao, Y. (eds) Emerging Technologies for Education. SETE 2016. Lecture Notes in Computer Science(), vol 10108. Springer, Cham. https://doi.org/10.1007/978-3-319-52836-6_60

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  • DOI: https://doi.org/10.1007/978-3-319-52836-6_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-52835-9

  • Online ISBN: 978-3-319-52836-6

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