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Cluster analysisa primer using R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Cluster analysisLior Rokach.
Reminder of title:	a primer using R /
Author:	Rokach, Lior.
Published:	Singapore :World Scientific,c2025.
Description:	1 online resource (xix, 282 p.) :ill.
Subject:	Cluster analysis.
Online resource:	https://www.worldscientific.com/worldscibooks/10.1142/13968#t=toc
ISBN:	9789811297489$q(ebook for institutions)

Cluster analysisa primer using R /
Rokach, Lior.

Cluster analysisa primer using R /[electronic resource] :Lior Rokach. - 1st ed. - Singapore :World Scientific,c2025. - 1 online resource (xix, 282 p.) :ill.

Includes bibliographical references and index.

Introduction to data clustering -- Similarity measures -- Partitioning methods for minimizing distance measures -- Hierarchical methods -- Clustering visualization -- Cluster validity : evaluation of clustering algorithms -- Mixture densities-based clustering -- Graph clustering -- Grid-based clustering methods -- Deep learning for clustering -- Spectral clustering.

"Cluster analysis is a fundamental data analysis task that aims to group similar data points together, revealing the inherent structure and patterns within complex datasets. This book serves as a comprehensive and accessible guide, taking readers on a captivating journey through the foundational principles of cluster analysis. At its core, the book delves deeply into various clustering algorithms, covering partitioning methods, hierarchical methods, and advanced techniques such as mixture density-based clustering, graph clustering, and grid-based clustering. Each method is presented with clear, concise explanations, supported by illustrative examples and hands-on implementations in the R programming language - a popular and powerful tool for data analysis and visualization. Recognizing the importance of cluster validation and evaluation, the book devotes a dedicated chapter to exploring a wide range of internal and external quality criteria, equipping readers with the necessary tools to assess the performance of clustering algorithms. For those eager to stay at the forefront of the field, the book also presents deep learning-based clustering methods, showcasing the remarkable capabilities of neural networks in uncovering hidden structures within complex, high-dimensional data. Whether you are a student seeking to expand your knowledge, a data analyst looking to enhance your toolbox, or a researcher exploring the frontiers of data analysis, this book will provide you with a solid foundation in cluster analysis and empower you to tackle a wide range of data-driven problems"--

ISBN: 9789811297489$q(ebook for institutions)Subjects--Topical Terms:

182711
Cluster analysis.

LC Class. No.: QA278 / .R65 2025

Dewey Class. No.: 519.5/302855133

Cluster analysisa primer using R /
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100 1 $a Rokach, Lior. $3 253733
245 1 0 $a Cluster analysis $h [electronic resource] : $b a primer using R / $c Lior Rokach.
250 $a 1st ed.
260 $a Singapore : $b World Scientific, $c c2025.
300 $a 1 online resource (xix, 282 p.) : $b ill.
504 $a Includes bibliographical references and index.
505 2 $a Introduction to data clustering -- Similarity measures -- Partitioning methods for minimizing distance measures -- Hierarchical methods -- Clustering visualization -- Cluster validity : evaluation of clustering algorithms -- Mixture densities-based clustering -- Graph clustering -- Grid-based clustering methods -- Deep learning for clustering -- Spectral clustering.
520 $a "Cluster analysis is a fundamental data analysis task that aims to group similar data points together, revealing the inherent structure and patterns within complex datasets. This book serves as a comprehensive and accessible guide, taking readers on a captivating journey through the foundational principles of cluster analysis. At its core, the book delves deeply into various clustering algorithms, covering partitioning methods, hierarchical methods, and advanced techniques such as mixture density-based clustering, graph clustering, and grid-based clustering. Each method is presented with clear, concise explanations, supported by illustrative examples and hands-on implementations in the R programming language - a popular and powerful tool for data analysis and visualization. Recognizing the importance of cluster validation and evaluation, the book devotes a dedicated chapter to exploring a wide range of internal and external quality criteria, equipping readers with the necessary tools to assess the performance of clustering algorithms. For those eager to stay at the forefront of the field, the book also presents deep learning-based clustering methods, showcasing the remarkable capabilities of neural networks in uncovering hidden structures within complex, high-dimensional data. Whether you are a student seeking to expand your knowledge, a data analyst looking to enhance your toolbox, or a researcher exploring the frontiers of data analysis, this book will provide you with a solid foundation in cluster analysis and empower you to tackle a wide range of data-driven problems"-- $c Provided by publisher.
588 $a Description based on print version record.
650 0 $a Cluster analysis. $3 182711
650 0 $a Cluster analysis $x Data processing. $3 182449
650 0 $a R (Computer program language) $3 210846
856 4 0 $u https://www.worldscientific.com/worldscibooks/10.1142/13968#t=toc