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Methods in Hard and Fuzzy Clustering

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Soft Computing and Human-Centered Machines

Part of the book series: Computer Science Workbench ((WORKBENCH))

Abstract

Clustering, also referred to as cluster analysis, is a class of unsupervised classification methods for data analysis. There have been numerous studies of clustering, which are both theoretical and applicational. Applications to scientific classifications, engineering problems, behavioral sciences. etc., have been investigated and usefulness of this technique has been appreciated.

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Authors
  1. Sadaaki Miyamoto
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  2. Kazutaka Umayahara
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Software Engineering, The University of Melbourne, 3052, Parkville, VIC, Australia

    Zhi-Qiang Liu (Professor) (Professor)

  2. Institute of Engineering Mechanics and Systems, The University of Tsukuba, 305-8573, Ibaraki, Japan

    Sadaaki Miyamoto (Professor) (Professor)

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© 2000 Springer-Verlag Tokyo

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Miyamoto, S., Umayahara, K. (2000). Methods in Hard and Fuzzy Clustering. In: Liu, ZQ., Miyamoto, S. (eds) Soft Computing and Human-Centered Machines. Computer Science Workbench. Springer, Tokyo. https://doi.org/10.1007/978-4-431-67907-3_5

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  • DOI: https://doi.org/10.1007/978-4-431-67907-3_5

  • Publisher Name: Springer, Tokyo

  • Print ISBN: 978-4-431-70279-5

  • Online ISBN: 978-4-431-67907-3

  • eBook Packages: Springer Book Archive

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