Learning collaboration links in a collaborative fuzzy clustering environment

Authors:
Rafael Falcon;Gwanggil Jeon;Rafael Bello;Jechang Jeong
Affiliations:
Computer Science Department, Universidad Central de Las Villas, Santa Clara, Cuba;Dept. of Electronics and Computer Engineering, Hanyang University, Seoul, Korea;Computer Science Department, Universidad Central de Las Villas, Santa Clara, Cuba;Dept. of Electronics and Computer Engineering, Hanyang University, Seoul, Korea
Venue:
MICAI'07 Proceedings of the artificial intelligence 6th Mexican international conference on Advances in artificial intelligence
Year:
2007

Citing 8
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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.