Customer Reviews

Predictive Data Mining: A Practical Guide (The Morgan Kaufmann Series in Data Management Systems)

5 star:		(3)
4 star:		(3)
3 star:		(2)
2 star:		(0)
1 star:		(0)

 

 

This isn't a bad book to pick up if you want to find out what data mining is about. I did, and it served as a good introduction.

For those of you who, like me, don't know what this is about, let me try to summarize. For years, organizations have been collecting a lot of information, via computer, just to run their business. For legal and business reasons, they have had to hold on to it, long past what they considered to be its useful life. But other than just storing it, what good is it?

Well, someone decided it could be used to answer questions about the business. Enter the data warehouse. The idea is to take all this old data, clean it up, and put it in a large database. Then the data can be mined for information.

There are two functions of data mining. One is to answer questions about the business. The other is to discover new knowledge about the business that you did not even have the sense to put in the form of a question. Everything from simple statistics to neural nets, genetic algorithms, and evolutionary programs can be used to mine the data.

Like any other science, this can be used for good purposes (what's the main reason homeless people become homeless), or bad (who's most likely to buy the Brooklyn Bridge). It can be used in many areas of science, although I suspect it will mostly be used by businesses trying to take marketing where no man has gone before.

The book itself is mostly prose, so it's an easy read, although it does require some computer knowledge. The more technical sections (like k-means and entropy clustering) are awkwardly written. But this does not detract from the overall effectiveness of the book.

If you're a manager whose boss just told you to head up the data warehouse, and you don't have a clue what he's talking about, this wouldn't be a bad book to get.

I give it a 7. It's easy to dance to.

I felt that this was an excellent intorduction to an area otherwise overcrowded with untested and semi validated "methodologies". I loved the fact that this title is not vendor specific (unlike some other titles trying to sell you tools or what!). Seeing the MKP name on it is reassuring. Would'nt hesitate to recommend this one!

I found this book to be an excellent description of the entire life cycle of data mining. It does not attempt to give detailed descriptions of the technical features of various methods, instead referring to relevant books and articles.

Those who are interested in undertaking data mining will need to obtain some of the mentioned references, a book that includes technical details, or a data mining software package.

The methods listed in the book have been implemented in some software that is separately available. My only disappointment with Predictive Data Mining is with this software - it is so poorly documented (input and output) that it is virtually useless.

In summary, those wanting a "managerial overview" of data mining will certainly gain it from this book. Those wanting actually to do data mining will need a technical book or some software (but not this book's software).

If you are new to the field I definitely think this is a good introduction to the main topics revolving around getting more out of your data. It gives you a nice flavor of several techniques used throughout all process of knowledge discovery (and not only mining techniques). Moreover, if bought with the software option you can quickly try several methods on your data. The book is very easy to read in spite of addressing some though research problems.

Thoughtful and readable introduction to data mining. It is a useful primer and refreshingly devoid of the buzzword afflictions of other books on this topic.

This book covers many different types of techniques for data mining but doesn't give lots of details on some of the techniques. I was looking for inductive learning algorihtms (ID3, C4.5, C5.0) and didn't find them here. For the techniques with depth the depth is in the form of formulas rather than examples.

A dry read. Great concepts. But you almost need supplementary material with more depth if you want to get a handle around all that it tries to cover. I picked it up because I thought it would be a good and easy refresher. Good it was, easy it was not. More practical examples would have helped. The best chapter was the last chapter when you finally see some real world application of the concepts described in the book.

I highly recommend this book, having reviewed it for "PC AI" magazine, and frequently using it as a reference since. Coverage is current and practical.