Finding Groups in Data: An Introduction to Cluster Analysis

by Leonard Kaufman and Peter J. Rousseeuw

Book Overview

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.

• Format: Hardcover
• Language: English
• ISBN: 0471878766
• ISBN13: 9780471878766
• Release Date: March 1990
• Publisher: Wiley-Interscience
• Length: 342 Pages
• Weight: 1.60 lbs.
• Dimensions: 0.9" x 6.3" x 9.3"

Customer Reviews

modern treatment of clustering theory and algorithms

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.

Excellent Book in Cluster Analysis

For those readers complaining about the source code, they are currently implemented in S-Plus (and probably in the free public R project). The current S-PLUS codes are very user friendly. If you have the S-Plus manual, you dont need this book for simple routine. Only serious statisticians, who needs in-depth information, would love this book.

Very good comparison of cluster methods - coded in SPlus.

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!!!