Finding Groups in Data: An Introduction to Cluster Analysis [Hardcover]

Leonard Kaufman (Author), Peter J. Rousseeuw (Author)

Book Description

Publication Date: March 1990 | ISBN-10: 0471878766 | ISBN-13: 978-0471878766 | Edition: 99

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

"Cluster analysis is the increasingly important and practical subject of finding groupings in data. The authors set out to write a book for the user who does not necessarily have an extensive background in mathematics. They succeed very well."
—Mathematical Reviews

"Finding Groups in Data [is] a clear, readable, and interesting presentation of a small number of clustering methods. In addition, the book introduced some interesting innovations of applied value to clustering literature."
—Journal of Classification

"This is a very good, easy-to-read, and practical book. It has many nice features and is highly recommended for students and practitioners in various fields of study."
—Technometrics

An introduction to the practical application of cluster analysis, this text presents a selection of methods that together can deal with most applications. These methods are chosen for their robustness, consistency, and general applicability. This book discusses various types of data, including interval-scaled and binary variables as well as similarity data, and explains how these can be transformed prior to clustering.

Editorial Reviews

From the Publisher

An introduction to the practical application of cluster analysis, this text presents a selection of methods which together can deal with most applications. These methods are chosen for their robustness, consistency and general applicability. Discusses the main approaches to clustering and provides guidance in choosing between the available methods. Also discusses various types of data, including interval-scaled and binary variables as well as similarity data and explains how these can be transformed prior to clustering. Contains numerous exercises.

From the Back Cover

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists.

"Cluster analysis is the increasingly important and practical subject of finding groupings in data. The authors set out to write a book for the user who does not necessarily have an extensive background in mathematics. They succeed very well."
—Mathematical Reviews

"Finding Groups in Data [is] a clear, readable, and interesting presentation of a small number of clustering methods. In addition, the book introduced some interesting innovations of applied value to clustering literature."
—Journal of Classification

"This is a very good, easy-to-read, and practical book. It has many nice features and is highly recommended for students and practitioners in various fields of study."
—Technometrics

An introduction to the practical application of cluster analysis, this text presents a selection of methods that together can deal with most applications. These methods are chosen for their robustness, consistency, and general applicability. This book discusses various types of data, including interval-scaled and binary variables as well as similarity data, and explains how these can be transformed prior to clustering. --This text refers to the Paperback edition.

See all Editorial Reviews

Product Details

• Hardcover: 368 pages
• Publisher: Wiley-Interscience; 99 edition (March 1990)
• Language: English
• ISBN-10: 0471878766
• ISBN-13: 978-0471878766
• Product Dimensions: 9.3 x 6.3 x 0.9 inches
• Shipping Weight: 1.5 pounds
• Average Customer Review: 3.0 out of 5 stars  See all reviews (7 customer reviews)
• Amazon Best Sellers Rank: #2,353,789 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

26 of 26 people found the following review helpful:

4.0 out of 5 stars modern treatment of clustering theory and algorithms, March 30, 2008

By 

Michael R. Chernick "statman31147" (Holland PA) - See all my reviews
(REAL NAME)   

This review is from: Finding Groups in Data: An Introduction to Cluster Analysis (Wiley Series in Probability and Statistics) (Paperback)

Creating clusters for data is a very old exploratory technique. This text provides a clear up-to-date and modern approach to this multivariate exploratory data analysis technique.

12 of 12 people found the following review helpful:

4.0 out of 5 stars Very good comparison of cluster methods - coded in SPlus., January 4, 1999

By A Customer

This review is from: Finding Groups in Data: An Introduction to Cluster Analysis (Hardcover)

I enjoyed the close comparison and some of the evaluations of the competing clustering methods. Particularly informative were the discussions of the underlying data distribution assumptions. It also was of even more use because the implementation of the algorithms has been accomplished in S-Plus (MathSoft - 12/98->v.4 ). Later chapters look at some of the model-based statistics which are thus made available to the biostatistic community. The book is particularly useful to make understandable the assumptions which can be applied to larger datasets. Such datasets are prevalent in bioinformatics, from microarray and PCR analyses -- such as P. Brown's and Somogyi's work. Tools to implement the algorithms in this book make it valuable since S-Plus is available to the academic community. Tremendously powerful understanding with this pairing of academic treatment and industrial-strength modelling software!!!

11 of 11 people found the following review helpful:

3.0 out of 5 stars Needs an update, April 15, 2003

By A Customer

This review is from: Finding Groups in Data: An Introduction to Cluster Analysis (Hardcover)

This book is well written, and it can be a useful reference for several standard clustering procedures. The software code (on paper only) is very out of date, and essentially useless. The algorithms have been implemented in Splus (and also in R), and since the documentation in Splus is really inadequate, this text should be considered the documentation.

I hope the authors will write a second edition, and include software, examples, and data on a companion disk. The quality of writing is very good, and there are a few things in this book that I cannot find in any other reference.