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Sinopsis
Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.
This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.
Real life examples are used throughout to demonstrate the application of the theory, and figures are used extensively to illustrate graphical techniques. The book is comprehensive yet relatively non-mathematical, focusing on the practical aspects of cluster analysis.
Key Features:
- Presents a comprehensive guide to clustering techniques, with focus on the practical aspects of cluster analysis
- Provides a thorough revision of the fourth edition, including new developments in clustering longitudinal data and examples from bioinformatics and gene studies./li>
- Updates the chapter on mixture models to include recent developments and presents a new chapter on mixture modeling for structured data
Practitioners and researchers working in cluster analysis and data analysis will benefit from this book.
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Acerca del autor
Brian S. Everitt, Head of the Biostatistics and Computing Department and Professor of Behavioural Statistics, Kings College London. He has authored/ co-authored over 50 books on statistics and approximately 100 papers and other articles, and is also joint editor of Statistical Methods in Medical Research.
Dr Sabine Landau, Head of Department of Biostatistics, Institute of Psychiatry, Kings College London.
Dr Morven Leese, Health Service and Population Research, Institute of Psychiatry, Kings College London.
Dr Daniel Stahl, Deptartment of Biostatistics & Computing, Institute of Psychiatry, Kings College London.
De la contraportada
Cluster Analysis: 5th Edition
Brian S. Everitt, Professor Emeritus, King's College, London, UK
Sabine Landau, Morven Leese and Daniel Stahl, Institute of Psychiatry, King's College London, UK
Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.
This 5th edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.
Real life examples are used throughout to demonstrate the application of the theory, and figures are used extensively to illustrate graphical techniques. The book is comprehensive yet relatively non-mathematical, focusing on the practical aspects of cluster analysis.
Key Features:
• Presents a comprehensive guide to clustering techniques, with focus on the practical aspects of cluster analysis.
• Provides a thorough revision of the fourth edition, including new developments in clustering longitudinal data and examples from bioinformatics and gene studies
• Updates the chapter on mixture models to include recent developments and presents a new chapter on mixture modeling for structured data.
Practitioners and researchers working in cluster analysis and data analysis will benefit from this book.
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Cluster Analysis (Hardcover)
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John Wiley & Sons Inc, New York, 2011
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ISBN 13: 9780470749913
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Hardcover. Condición: new. Hardcover. Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics. This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data. Real life examples are used throughout to demonstrate the application of the theory, and figures are used extensively to illustrate graphical techniques. The book is comprehensive yet relatively non-mathematical, focusing on the practical aspects of cluster analysis. Key Features: Presents a comprehensive guide to clustering techniques, with focus on the practical aspects of cluster analysisProvides a thorough revision of the fourth edition, including new developments in clustering longitudinal data and examples from bioinformatics and gene studies./li>Updates the chapter on mixture models to include recent developments and presents a new chapter on mixture modeling for structured data Practitioners and researchers working in cluster analysis and data analysis will benefit from this book. * Presents a comprehensive guide to clustering techniques. * Provides a thorough revision of the fourth edition, including new developments in clustering longitudinal data and examples from bioinformatics and gene studies * Includes a new section on how to use R for cluster analysis. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Nº de ref. del artículo: 9780470749913
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