Книга Finding Groups in Data - Introduction to Cluster Analysis Leonard Kaufman

Finding Groups in Data - Introduction to Cluster Analysis

Език: Английски език
Корици: С меки корици
Издател: John Wiley & Sons Inc
Наличност: Външен склад
Изпращаме след 9-15 дни
141.58 276.90 лв
The Wiley-Interscience Paperback Series consists of selected books that have been made more accessib...

Информация за книгата

Език
Английски език
Корици
Книга - С меки корици
Издадена
2005
страници
342
EAN
9780471735786
ISBN
0471735787
Enbook ID
04893536
Издател
Теглоt
500
Размери
157 x 234 x 17

Пълно описание

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.

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