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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Paperback. Condition: New. Print on Demand. This book presents a novel clustering procedure applicable to both variable-by-case data matrices and case-by-case dissimilarity matrices. Unlike many existing methods that approach clustering as a heuristic, the proposed procedure is based on a uniformly consistent density estimate, making it useful for drawing statistical inferences about the underlying population from a sample. The author demonstrates the asymptotic consistency of the method for high-density clusters in several dimensions and illustrates its small-sample behavior through empirical examples. A real-world application showcases the practical utility of this method, which provides valuable insights into identifying high-density clusters in data. This book is a reproduction of an important historical work, digitally reconstructed using state-of-the-art technology to preserve the original format. In rare cases, an imperfection in the original, such as a blemish or missing page, may be replicated in the book. print-on-demand item. Seller Inventory # 9781332267262_0
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