Learning collaboration links in a collaborative fuzzy clustering environment
- Authors
- Falcon, Rafael; Jeon, Gwanggil; Bello, Rafael; Jeong, Jechang
- Issue Date
- Nov-2007
- Publisher
- Springer Verlag
- Keywords
- Collaboration links; Collaborative fuzzy clustering; Evolutionary computation; Information granules; Particle swarm optimization; Rough set theory
- Citation
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.4827 LNAI, pp.483 - 495
- Indexed
- SCOPUS
- Journal Title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- Volume
- 4827 LNAI
- Start Page
- 483
- End Page
- 495
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/179353
- DOI
- 10.1007/978-3-540-76631-5_46
- ISSN
- 0302-9743
- 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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