Finding Groups in Data: An Introduction to Cluster Analysis
by Leonard Kaufman; Peter J. RousseeuwFormat: eBook
Pub. Date: 2009-09-01
Publisher(s): Wiley-Interscience
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Summary
An introduction to the practical application of cluster analysis,Finding Groups in Data presents a selection of methods that together can deal with most applications. These methods are chosen for their robustness, consistency, and general applicability. The text discusses the main approaches to clustering and provides guidance in choosing between the available methods. It 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. With numerous exercises to aid learning,Finding Groups in Data provides an invaluable introduction to cluster analysis with an emphasis on methods that are both easy to use and modern.
Table of Contents
| Partitioning Around Medoids (Program PAM) | |
| Clustering Large Applications (Program CLARA) | |
| Fuzzy Analysis (Program FANNY) | |
| Agglomerative Nesting (Program AGNES) | |
| Divisive Analysis (Program DIANA) | |
| Monothetic Analysis (Program MONA) | |
| Appendices | |
| References | |
| Author Index | |
| Subject Index | |
| Table of Contents provided by Publisher. All Rights Reserved. |
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