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Excerpt from A Kth Nearest Neighbour Clustering Procedure
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. A real application is also included to demonstrate the practical utility of this clustering method.
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