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Term Clustering in Texts Based on Fuzzy Neighborhoods and Kernel Functions

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4693))

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Abstract

A fuzzy neighborhood model for analyzing information systems having topological structures on occurrences of keywords is proposed and associated kernel functions are derived. Sufficient conditions when a neighborhood defines a kernel are described. Clustering algorithms with and without a kernel function are developed. Illustrative examples are given.

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Author information

Authors and Affiliations

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

    Sadaaki Miyamoto

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

    Satoshi Hayakawa & Yuichi Kawasaki

Authors
  1. Sadaaki Miyamoto
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  2. Satoshi Hayakawa
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  3. Yuichi Kawasaki
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Editor information

Editors and Affiliations

  1. Dipartimento di Scienze dell’Informazione, Università degli Studi di Milano, Via Comelico 39/41, 20135, Milano, Italy

    Bruno Apolloni

  2. Centre for SMART Systems, School of Engineering, University of Brighton, BN2 4GJ, Brighton, UK

    Robert J. Howlett

  3. Knowledge-Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes, SA 5095, Adelaide, Australia

    Lakhmi Jain

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© 2007 Springer-Verlag Berlin Heidelberg

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Miyamoto, S., Hayakawa, S., Kawasaki, Y. (2007). Term Clustering in Texts Based on Fuzzy Neighborhoods and Kernel Functions. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4693. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74827-4_65

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  • DOI: https://doi.org/10.1007/978-3-540-74827-4_65

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-74826-7

  • Online ISBN: 978-3-540-74827-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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