Cluster Analysis

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9780470749913: Cluster Analysis

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
· 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.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

From the Publisher :

An accessible and practical introduction to clustering using a minimum of mathematics. This extensively revised edition contains detailed descriptions of the latest methods along with numerous examples and updated information on available software packages. Closing chapters provide suggestions which will be helpful in many situations when applying clustering or evaluating results.

From the Back Cover :

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.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

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Brian S. Everitt, Sabine Landau, Morven Leese
Edité par John Wiley and Sons Ltd, United States (2011)
ISBN 10 : 0470749911 ISBN 13 : 9780470749913
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Description du livre John Wiley and Sons Ltd, United States, 2011. Hardback. État : New. 5th Revised edition. Language: English . Brand New Book. 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 * 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. N° de réf. du libraire AAH9780470749913

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Brian S. Everitt, Sabine Landau, Morven Leese
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ISBN 10 : 0470749911 ISBN 13 : 9780470749913
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Description du livre John Wiley and Sons Ltd, United States, 2011. Hardback. État : New. 5th Revised edition. Language: English . Brand New Book. 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 * 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. N° de réf. du libraire AAH9780470749913

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Brian S. Everitt,Sabine Landau,Morven Leese,Daniel Stahl
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