7 of 7 people found the following review helpful
on 6 July 2009
This book has reasonable coverage of the basic algorithms used in simple data mining. However, it has the huge flaws that:
(a) Everything is introduced in a vague, hand-wavy kind of way, without any kind of precision. I know that mathematics is a turn-off for some people, but how many of those people are going to be trying to learn about data-mining algorithms? More precision is required. As a result of its lack complexities are hidden away..E.g. training neural networks is made to look simple, and there's a throw-away comment that back-propogation is not now used, another, undefined and unreferenced, algorithm being preferred.
(b) There's too much waffle about why you might want to mine data. That's fine, and there's reason for a book about precisely that to be written. But not the same book that explains what K-means clustering is. So there are two books here, a (possibly) good book about why a business might want to data mine, and the pitfalls etc it might experience, and a (very) bad book about the mechanics of data mining.
So don't buy it if you want to learn about data mining. For an elementary, but rigorous, introduction, may I recommend Bramer's 'Principles of Data Mining'.
3 of 3 people found the following review helpful
on 6 July 2007
Anyone interested in automating and improving decisions should have this book. It is one of the classic works on data mining and well worth the read.
I really liked the book both because it is well written and because, although it drilled into a fair amount of detail about some of the techniques, it started each new section off at a high level. This allows someone without a statistical background, such as me, to read as far as I can in each section and then skip ahead to the next technique. This is a nice change from books that simply get more and more detailed as page follows page, preventing you from gaining an overview of the subject.
The book introduces data mining and a methodology for applying it, talks about some of the applications in "Marketing, Sales, and Customer Relationship Management" (as the subtitle puts it), walks through some statistical techniques and then spends the bulk of the book on various data mining techniques. It wraps up with a nice summary of how data mining plays with other technologies and with some practical advice on getting started.
One of the best summaries of where data mining fits is given early in the book where an enterprise is encouraged to:
- Notice what its customers are doing
- Remember what it and its customers have done over time
- Learn from what it has remembered
- Act on what if has learned to make customers more profitable
The authors point out that Data Mining is focused on the "Learn" stage or, as they put it data mining suggests but businesses decide.
The methodology section, and the subsequent notes that relate to applying these techniques in real life, talked about the feedback loops between steps in data mining - there is not a linear "waterfall" sequence of steps but constant iteration and learning. They also emphasized the importance of finding the right business problem at the beginning - start as someone once said, with the end in mind. This was reiterated when they quote Voltaire who said "Le mieux est l'ennemi du bien" ("The best is the enemy of good"). In other words, don't get hung up on trying to find the perfect algorithm, perfect answer. Instead build something that is good, that works, and learn and improve over time.
The authors made a big point out of the value of data mining for "mass intimacy", where you want to treat customers differently and there is a business reason to do so but where customers are too numerous to be assigned to staff. One of the issues they pointed out was that staff must be trained in customer interaction skills while also using all the data you have. The value of data mining in building a customer-centric organization cannot be overestimated.
3 of 3 people found the following review helpful
on 13 May 2007
This book does what few others manage - namely, go through an immense amount of material using almost no math at all, so it's a pleasure to read, and discussing not just what the techniques are, but what they do, what they're good for, and what weaknesses each has.
On the other hand, the book gives enough detail on each method that it's completely clear how the math goes, and I could (and did) write the math easily for the methods I was interested in.
on 13 April 2009
No equations nor mathematical proofs, if you want those, steer away from this book. Otherwise, it is filled with clear intuitive explanations, with real-life business / social applications. I strongly recommend this book I have already bought it for several colleagues and professionals.