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Agglomerative Clustering Using Asymmetric Similarities

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Modeling Decision for Artificial Intelligence (MDAI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6820))

Abstract

Algorithms of agglomerative hierarchical clustering using asymmetric similarity measures are studied. Two different measures between two clusters are proposed, one of which generalizes the average linkage for symmetric similarity measures. Asymmetric dendrogram representation is considered after foregoing studies. It is proved that the proposed linkage methods for asymmetric measures have no reversals in the dendrograms. Examples based on real data show how the methods work.

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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. Artificial Intelligence Research Institute (IIIA) Spanish National Research Council (CSIC), IIIA-CSIC, Campus Universitat Autonoma de Barcelona, 08193, Bellaterra, Catalonia, Spain

    Vicenç Torra

  2. Toho Gakuen, 3-1-10, Naka, Kunitachi, 184-0004, Tokyo, Japan

    Yasuo Narakawa

  3. School of Computer, National University of Defense Technology, 410073, Changsha, China

    Jianping Yin

  4. Department of Network Engineering, National University of Defense Technology, Yanwachi Street 137, 410073, Changsha, Hunan, China

    Jun Long

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© 2011 Springer-Verlag Berlin Heidelberg

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Takumi, S., Miyamoto, S. (2011). Agglomerative Clustering Using Asymmetric Similarities. In: Torra, V., Narakawa, Y., Yin, J., Long, J. (eds) Modeling Decision for Artificial Intelligence. MDAI 2011. Lecture Notes in Computer Science(), vol 6820. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22589-5_12

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  • DOI: https://doi.org/10.1007/978-3-642-22589-5_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22588-8

  • Online ISBN: 978-3-642-22589-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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