This practical text has been reworked to provide an account of the subject of cluster analysis - the generic name for a wide variety of procedures involved with identifying groups within data. By organizing multivariate data into such subgroups clustering may help the investigator discover the characteristics of any structure or pattern present. However, applying the methods in practice requires considerable care and researchers may need guidance in order to avoid misinterpreting results. It offers an applied approach using limited mathematics, contains detailed descriptions of recent methods along with numerous examples and includes information on the software packages available. Other titles by Brian S. Everitt include "Talking About Statistics: A Psychologist's Guide to Design and Analysis" (with Dale F. Hay), "Applied Multivariate Data Analysis" (with Graham Dunn) and "Statistical Methods for Medical Investigations".
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An accessible and practical introduction to clustering using a minimum of mathematics. This extensively revised edition contains detailed descriptions of the latest methods along with numerous examples and updated information on available software packages. Closing chapters provide suggestions which will be helpful in many situations when applying clustering or evaluating results.
Cluster Analysis: 5th Edition
Brian S. Everitt, Professor Emeritus, King's College, London, UK
Sabine Landau, Morven Leese and Daniel Stahl, Institute of Psychiatry, King's College London, UK
Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.
This 5th edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.
Real life examples are used throughout to demonstrate the application of the theory, and figures are used extensively to illustrate graphical techniques. The book is comprehensive yet relatively non-mathematical, focusing on the practical aspects of cluster analysis.
Key Features:
· Presents a comprehensive guide to clustering techniques, with focus on the practical aspects of cluster analysis.
· Provides a thorough revision of the fourth edition, including new developments in clustering longitudinal data and examples from bioinformatics and gene studies
· Updates the chapter on mixture models to include recent developments and presents a new chapter on mixture modeling for structured data.
Practitioners and researchers working in cluster analysis and data analysis will benefit from this book.
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