Using the Kth Nearest Neighbor Clustering Procedure to Determine the Number of Subpopulations (Classic Reprint) - Softcover
Synopsis
Discover how to uncover hidden subpopulations in data with a practical, study‑ready method.
This guide explains a kth nearest neighbor clustering approach to count and test the number of modes in a univariate density, helping you see when a population is unimodal, bimodal, or more complex.
The book walks you through a clear procedure: estimate a density with a chosen k, use hierarchical clustering to identify modes, and apply a bootstrap test to assess significance. It emphasizes when the method works best and where small or uneven subpopulations can affect results, all in accessible, data‑driven terms.- How the diagnostic plot signals the number of modes and possible substructures in a data set.
- How to use the bootstrap‑based p‑value to decide if observed multimodality is statistically significant.
- Practical notes on sample size effects, computation time, and limitations in higher dimensions.
- Illustrative examples from real data to show interpretation in context.
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Paperback. Condition: New. Print on Demand. This book introduces the kth nearest neighbor clustering procedure, a groundbreaking advancement in cluster analysis, the science of organizing data by similarities to discover patterns. The kth nearest neighbor clustering procedure is a powerful tool for detecting subpopulations within data, offering a robust and consistent approach to cluster identification. The author explores the theoretical foundations and practical applications of the kth nearest neighbor clustering procedure, providing a thorough understanding of its advantages and limitations. With a focus on its ability to identify modes within a population's density function, the book demonstrates how this procedure can uncover hidden structures within data. The author also delves into the statistical aspects of cluster analysis, discussing hypothesis testing and significance levels. The kth nearest neighbor clustering procedure is presented as an innovative tool for assessing the number of subpopulations present in a dataset, making it useful for researchers across various disciplines seeking to understand the underlying structure of their 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 # 9781332247530_0
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Using the Kth Nearest Neighbor Clustering Procedure to Determine the Number of Subpopulations Classic Reprint
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # LW-9781332247530
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Using the Kth Nearest Neighbor Clustering Procedure to Determine the Number of Subpopulations Classic Reprint
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000. Seller Inventory # LW-9781332247530
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