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k-Nearest-Neighbor Clustering and Percolation Theory

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Abstract

Let P be a realization of a homogeneous Poisson point process in ℝd with density 1. We prove that there exists a constant k d , 1<k d <∞, such that the k-nearest neighborhood graph of P has an infinite connected component with probability 1 when kk d . In particular, we prove that k 2≤213. Our analysis establishes and exploits a close connection between the k-nearest neighborhood graphs of a Poisson point set and classical percolation theory. We give simulation results which suggest k 2=3. We also obtain similar results for finite random point sets.

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Correspondence to Frances F. Yao.

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Part of the work was done while S.-H. Teng was at Xerox Palo Alto Research Center and MIT.

The work of F.F. Yao was supported in part by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China [Project No. CityU 1165/04E].

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Teng, SH., Yao, F.F. k-Nearest-Neighbor Clustering and Percolation Theory. Algorithmica 49, 192–211 (2007). https://doi.org/10.1007/s00453-007-9040-7

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  • DOI: https://doi.org/10.1007/s00453-007-9040-7

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