Cluster analysis

by Brian S. Everitt

Cluster analysis comprises a range of methods of classifying multivariate data into subgroups, and these techniques are widely applicable. The 4th edition of Cluster Analysis updates the successful 3rd edition and incorporates new material to cover developing areas, such as Bayesian statistics and neural networks. In addition, recent research on the evaluation of results and the assessment of the number of clusters has also been included. Real-life examples are used throughout to demonstrate the application of the theory, and graphical techniques are demonstrated with appropriate figures. Finally, this edition includes information on the software packages currently available. The author assumes some prior knowledge of statistics, but writes in a non-mathematical, accessible style. This concise book is ideal for postgraduate students of statistics, as well as researchers in medicine, sociology, and market research.

Genres: Mathematics, Academic

128 pages, Hardcover

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.