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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.
- Sales Rank: #194812 in Books
- Published on: 2011-02-21
- Original language: English
- Number of items: 1
- Dimensions: 9.25" h x .95" w x 6.38" l, 1.41 pounds
- Binding: Hardcover
- 346 pages
Review
... well structured and informative, providing detailed accounts of the new developments in the field. It is undoubtedly both an excellent inroduction to and a valuable reference work on cluster analysis. -- Swiss Journal of Psychology 20031001 ...this continues to be an excellent general introduction to an important and expanding topic. -- Short Book Reviews 20011101
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.
Most helpful customer reviews
16 of 18 people found the following review helpful.
Good introduction
By A Customer
Here is an excellent introduction to cluster analysis. The concepts are explained in clear language, with many illustrative examples. It is possibly the best of the introductory level books. I give it 4 stars because of a few misprints, and a few places where some essential information or detail has been omitted that can lead to misunderstanding.
4 of 4 people found the following review helpful.
Cluster Analysis at the Edge
By John M. Ford
I read this book as the text in a four-week online class on cluster analysis. I learned a great deal and do not regret purchasing this book. It has several strengths and some weaknesses as an introduction to this statistical technique.
There is a good introduction to the unsupervised learning problem of classifying objects into meaningful groups with no basis for validating these classifications. The authors' decision to focus on graphical methods early in the text is a good one and lays an intuitive foundation for their more technical presentation later in the book. The discussion of similarity measures at the core of cluster analysis is a good overview and prepares readers for more advanced discussions elsewhere.
The book closes with the highly useful and practical chapter "Some final comments and guidelines." It lists and describes nine steps in a typical cluster analysis and refers readers back to sections of the book which inform the decisions at each step. It's coverage of methods for testing cluster quality and the likelihood of no structure in a dataset is also accessible and of practical value. Readers might consider looking through this material before reading the previous chapters to help organize the information more meaningfully.
The middle chapters are worth reading, but suffer from a few problems. In general, these chapters are better at describing the boundaries of current research in clustering techniques than they are in describing typical applications. There are too many research results and not enough examples. The examples that are included are described too briefly, making it difficult to follow how the analysis was carried out. Better integration of citations in the body of the text would be a key improvement. As would inclusion of sample exercises with worked-out solutions in an appendix.
Recognizing the difficulty of making a statistics text accessible to readers using a variety of software packages, I still believe this was not done well in this book. See Iain Pardoe's Applied Regression Modeling for one example of how to do this very well. I will hope for improvements in a later edition of this book.
This book has challenges as a text, but was worth the price and the time spent with it. Still, I will be on the lookout for a better alternative.
3 of 3 people found the following review helpful.
Old might still be gold
By Vishnu K Lagoo
I own acopy of the second edition of presumably the same book published in 1980. But its author is Brian Everitt alone and it has 132 pages. Obviously, the current edition discusses the more recent developments in Clustering Analysis and it has two more co-authors. So I might miss on newer definitions, but I have to say that the author's explanation of the then available methods in the old edition is of excellent quality.
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