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Siegelmann, and V. Vapnik: Support vector clustering. Journal of Machine Learning Research, 2001, 125–137. 7. K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft: When is nearest neighbor meaningful. In: Proceedings of 7th International Conference on Database Theory, 1999, 217–235. 8. J. Bezdek: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York, 1981. 9. C. Bishop: Neural networks for pattern recognition. Oxford University Press, 1995. Computational Intelligence in Clustering Algorithms 47 10.

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An algorithm for the computation of h(P ) is presented in [5]. This algorithm is based on the idea of view frustum [2], which is used in 3D graphics for culling away 3D objects. In [5], it is employed to determine a finite sequence of source points” {Qi } starting from PC , and a corresponding partition of Ω, {Ωi }. Each source point is the farthest visible point on ∂Ω from its predecessor in the sequence. The sequence of source points determines a partition of Ω into subdomains, such that each subdomain Ωi is the maximal region in Ω \ ∪i−1 j=1 Ωj which is visible from Qi .

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