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Agglomerative Hierarchical Clustering Using Asymmetric Similarity Based on a Bag Model and Application to Information on the Web

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Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7027))

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Abstract

An algorithm of agglomerative hierarchical clustering using an asymmetric similarity measure based on a bag model is proposed. This bag model is studied for document clustering and analysis of information on the web. The definition of an inter-cluster similarity is proposed and a dendrogram output reflecting asymmetry of the similarity measure is shown. It is also proved that the dendrogram has no reversals. An example of word clusters on Twitter shows how the method works.

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Author information

Authors and Affiliations

  1. Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8573, Japan

    Satoshi Takumi

  2. Department of Risk Engineering, Faculty of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8573, Japan

    Sadaaki Miyamoto

Authors
  1. Satoshi Takumi
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  2. Sadaaki Miyamoto
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Editor information

Editors and Affiliations

  1. College of Computer Science, Zhejiang University, 310027, Hangzhou, Zhejiang Province, P.R. China

    Yongchuan Tang

  2. Japan Advanced Institute of Science and Technology (JAIST), 923-1292, Tatsunokuchi, Ishikawa, Japan

    Van-Nam Huynh

  3. Artificial Intelligence Group, Engineering Mathematics Department, University of Bristol, BS8 1TR, UK

    Jonathan Lawry

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© 2011 Springer-Verlag Berlin Heidelberg

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Takumi, S., Miyamoto, S. (2011). Agglomerative Hierarchical Clustering Using Asymmetric Similarity Based on a Bag Model and Application to Information on the Web. In: Tang, Y., Huynh, VN., Lawry, J. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2011. Lecture Notes in Computer Science(), vol 7027. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24918-1_21

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  • DOI: https://doi.org/10.1007/978-3-642-24918-1_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24917-4

  • Online ISBN: 978-3-642-24918-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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