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Learning Collaboration Links in a Collaborative Fuzzy Clustering Environment

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MICAI 2007: Advances in Artificial Intelligence (MICAI 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4827))

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Abstract

Revealing the common underlying structure of data spread across multiple data sites by applying clustering techniques is the aim of collaborative clustering, a recent and innovative idea brought up on the basis of exchanging information granules instead of data patterns. The strength of the collaboration between each pair of data repositories is determined by a user-driven parameter, both in vertical and horizontal collaborative fuzzy clustering. In this study, Particle Swarm Optimization and Rough Set Theory are used for setting the most suitable values of the collaboration links between the data sites. Encouraging empirical results uncovered the deep impact observed at the individual clusters, allowing us to conclude that the overall effect of the collaboration has been improved.

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Authors and Affiliations

  1. Computer Science Department, Universidad Central de Las Villas, Carretera Camajuaní km 5 Santa Clara, Cuba

    Rafael Falcon & Rafael Bello

  2. Dept. of Electronics and Computer Engineering, Hanyang University, 17 Haengdang-dong, Seongdong-gu, Seoul, Korea

    Gwanggil Jeon & Jechang Jeong

Authors
  1. Rafael Falcon
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  2. Gwanggil Jeon
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  3. Rafael Bello
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  4. Jechang Jeong
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Alexander Gelbukh Ángel Fernando Kuri Morales

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© 2007 Springer-Verlag Berlin Heidelberg

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Falcon, R., Jeon, G., Bello, R., Jeong, J. (2007). Learning Collaboration Links in a Collaborative Fuzzy Clustering Environment. In: Gelbukh, A., Kuri Morales, Á.F. (eds) MICAI 2007: Advances in Artificial Intelligence. MICAI 2007. Lecture Notes in Computer Science(), vol 4827. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76631-5_46

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  • DOI: https://doi.org/10.1007/978-3-540-76631-5_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76630-8

  • Online ISBN: 978-3-540-76631-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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