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Two Classes of Algorithms for Data Clustering

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Book cover 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

The two classes of agglomerative hierarchical clustering algorithms and K-means algorithms are overviewed. Moreover recent topics of kernel functions and semi-supervised clustering in the two classes are discussed. This paper reviews traditional methods as well as new techniques.

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Author information

Authors and Affiliations

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

    Sadaaki Miyamoto

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  1. Sadaaki Miyamoto
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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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Miyamoto, S. (2011). Two Classes of Algorithms for Data Clustering. 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_5

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  • DOI: https://doi.org/10.1007/978-3-642-24918-1_5

  • 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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