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International Symposium on Neural Networks

ISNN 2006: Advances in Neural Networks - ISNN 2006 pp 1222–1230Cite as

Adaptive Support Vector Clustering for Multi-relational Data Mining

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3971))

Abstract

A novel self Adaptive Support Vector Clustering algorithm (ASVC) is proposed in this paper to cluster dataset with diverse dispersions. And a Kernel function is defined to measure affinity between multi-relational data. Task of clustering multi-relational data is addressed by integrating the designed Kernel into ASVC. Experimental results indicate that the designed Kernel can capture structured features well and ASVC is of fine performance.

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Author information

Authors and Affiliations

  1. College of Computer Science, Jilin University, Key Laboratory of Symbol Computation, and Knowledge Engineering of the Ministry of Education, Changchun, 130012, China

    Ping Ling & Chun-Guang Zhou

  2. School of Computer Science, Xuzhou Normal University, Xuzhou, 221116, China

    Ping Ling

Authors
  1. Ping Ling
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  2. Chun-Guang Zhou
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Cite this paper

Ling, P., Zhou, CG. (2006). Adaptive Support Vector Clustering for Multi-relational Data Mining. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_181

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  • DOI: https://doi.org/10.1007/11759966_181

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34439-1

  • Online ISBN: 978-3-540-34440-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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