Finding Groups in Data: An Introduction to Cluster Analysis (Wiley Series in Probability and Statistics)

Kaufman, Leonard

ISBN 10: 0471735787 / ISBN 13: 9780471735786

Editore: Wiley-Interscience, 2005

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This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In fair condition, suitable as a study copy. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,550grams, ISBN:9780471735786.

Codice articolo 9759604

Dati bibliografici

• Titolo: Finding Groups in Data: An Introduction to Cluster Analysis (Wiley Series in Probability and Statistics)
• Editore: Wiley-Interscience
• Data di pubblicazione: 2005
• Lingua: Inglese
• ISBN 10: 0471735787
• ISBN 13: 9780471735786
• Rilegatura: Brossura
• Condizione: Fair
• Cataloghi dei venditori:
  • Mathematics

Descrizione articolo

Riassunto

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.

Informazioni sull?autore

LEONARD KAUFMAN, PhD, is affiliated with Vrije University in Brussels, Belgium.

PETER J. ROUSSEEUW, PhD, is a Professor in the Department of Mathematics and Computer Science at the University of Antwerp in Belgium.