Cluster Analysis

by Brian S. Everitt, Sabine Landau, Morven Leese

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.

Genres: Mathematics, Academic

352 pages, Kindle Edition

First published January 1, 1980

Reviews

[Darin · Rating 5 out of 5]

This book is an in depth presentation of clustering. Concepts are explained well. There aren't many books devoted entirely to cluster analysis, but this is the best of those I have seen.

[Chris · Rating 5 out of 5]

Great book to own as a professor. Lots of databases and real life examples, not to mention the bottomless supply of sources provided if one needs to dive deeper in any concept

[Otto Hahaa · Rating 4 out of 5]

This is a fifth edition, so it should be reasonable up-to-date. One can feel "geographical layers" in the text, some parts feel quite old-fashioned (I am thinking the neural network stuff with pictures of brains...), that might have been written differently today. Stranglely enough, it feels like most of the work was done in seventies and eighties, after that only some details have been polished. This means either that the field has reached the mature stage or something big is still missing. I would bet for the latter, and I think the authors would agree. In many places they emphasize that you really have to try different approaches and algorithms, and it is rather hard to say if your clusters are "correct" or at least not very wrong. Indeed, the basics of clustering is covered in many applied statistics books, but what is usually missing is methods how to evaluate the clusters. They are covered quite nicely here. Unfortunately, it is not always clear what metrics should be used, so again you should do some exploring.

But clustering is a lot of fun, and it is useful do some clustering at the start of your work, even if you will later not use the results for anything.