Cluster Analysis

by Brian S. Everitt

This First Edition of Statistics moves the curriculum in innovative ways while still looking relatively familiar. Statistics utilizes intuitive methods to introduce the fundamental idea of statistical inference. These intuitive methods are enabled through statistical software and are accessible at very early stages of a course. The text also includes the more traditional methods such as t-tests, chi-square tests, etc., but only after students have developed a strong intuitive understanding of inference through randomization methods. The text is designed for use in a one-semester introductory statistics course. The focus throughout is on data analysis and the primary goal is to enable students to effectively collect data, analyze data, and interpret conclusions drawn from data. The text is driven by real data and real applications. Although the only prerequisite is a minimal working knowledge of algebra, students completing the course should be able to accurately interpret statistical results and to analyze straightforward data sets.

Genres: Mathematics, Academic

136 pages, Paperback

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.