A kTH Nearest Neighbour Clustering Procedure

M. Anthony Wong and Tom Lane
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M. Anthony Wong: Massachusetts Institute of Technology
Tom Lane: Massachusetts Institute of Technology

A chapter in Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface, 1981, pp 308-311 from Springer

Abstract: Abstract Due to the lack of development in the probabilistic and statistical aspects of clustering research. clustering procedures are often regarded as heuristics generating artificial clusters from a given set of sample data. In this paper, a clustering procedure that is useful for drawing statistical inference about the underlying population from a random sample is developed. It is based on the uniformly consistent kth nearest neighbour density estimate. and is applicable to both case-by-variable data matrices and case-by-case dissimilarity matrices. The proposed clustering procedure is shown to be asymptotically consistent for high-density clusters in several dimensions. and its small-sample behavior is illustrated by empirical examples.

Keywords: high-density cluster; kth nearest neighbour density estimation; clustering procedure; set-consistency (search for similar items in EconPapers)
Date: 1981
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4613-9464-8_46

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DOI: 10.1007/978-1-4613-9464-8_46

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