1981 | OriginalPaper | Buchkapitel
A kTH Nearest Neighbour Clustering Procedure
verfasst von : M. Anthony Wong, Tom Lane
Erschienen in: Computer Science and Statistics: Proceedings of the 13th Symposium on the Interface
Verlag: Springer US
Enthalten in: Professional Book Archive
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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.