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 fifth 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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