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Clustering web documents using hierarchical representation with multi-granularity

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Abstract

Web document cluster analysis plays an important role in information retrieval by organizing large amounts of documents into a small number of meaningful clusters. Traditional web document clustering is based on the Vector Space Model (VSM), which takes into account only two-level (document and term) knowledge granularity but ignores the bridging paragraph granularity. However, this two-level granularity may lead to unsatisfactory clustering results with “false correlation”. In order to deal with the problem, a Hierarchical Representation Model with Multi-granularity (HRMM), which consists of five-layer representation of data and a two-phase clustering process is proposed based on granular computing and article structure theory. To deal with the zero-valued similarity problem resulted from the sparse term-paragraph matrix, an ontology based strategy and a tolerance-rough-set based strategy are introduced into HRMM. By using granular computing, structural knowledge hidden in documents can be more efficiently and effectively captured in HRMM and thus web document clusters with higher quality can be generated. Extensive experiments show that HRMM, HRMM with tolerance-rough-set strategy, and HRMM with ontology all outperform VSM and a representative non VSM-based algorithm, WFP, significantly in terms of the F-Score.

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Authors and Affiliations

  1. Faculty of Software, Fujian Normal University, Cangshan District, 8 Shangsan Road, Fuzhou, 350007, China

    Faliang Huang

  2. College of Computer Science and IT, Guangxi Normal University, Guilin, 541004, PR, China

    Shichao Zhang

  3. Computer Science, Aston University, Birmingham, Aston Triangle, B4 7ET, United Kingdom

    Minghua He

  4. Department of Computer Science, University of Vermont, 33 Colchester Avenue, Burlington, VT, 05405, USA

    Xindong Wu

  5. Faculty of Engineering and Information Technology, UTS, PO Box 123, Broadway, NSW, 2007, Australia

    Shichao Zhang

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  1. Faliang Huang
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  2. Shichao Zhang
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  3. Minghua He
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  4. Xindong Wu
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Correspondence to Shichao Zhang.

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Huang, F., Zhang, S., He, M. et al. Clustering web documents using hierarchical representation with multi-granularity. World Wide Web 17, 105–126 (2014). https://doi.org/10.1007/s11280-012-0197-x

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