Customer Reviews


38 Reviews
5 star:
 (15)
4 star:
 (15)
3 star:
 (3)
2 star:
 (1)
1 star:
 (4)
 
 
 
 
 
Average Customer Review
Share your thoughts with other customers
Create your own review
 
 

The most helpful favorable review
The most helpful critical review


87 of 89 people found the following review helpful
5.0 out of 5 stars Worthwhile Update to an Excellent Text
Context for this review: I am a data miner with 20 years experience, and own the first edition of this book.

Good:
- Accessible writing style
- Broad coverage of algorithms and data mining issues, with an eye toward practical issues
- Needless technical trivia (derivations and the like) are avoided
- Algorithms are completely spelled out: A...
Published on March 6, 2011 by William B. Dwinnell IV

versus
10 of 13 people found the following review helpful
1.0 out of 5 stars Avoid
Reading some of these reviews I feel like I must have gotten another book. I really didn't think the book was worth the time or money investment.

My main issues were:
1. 50% of the book covers WEKA
- but who is really going to use WEKA over a product like R.
- the WEKA coverage is mind numbingly bad. Lists of algorithm names without explanations...
Published 12 months ago by Charles


‹ Previous | 1 2 3 4 | Next ›
Most Helpful First | Newest First

87 of 89 people found the following review helpful
5.0 out of 5 stars Worthwhile Update to an Excellent Text, March 6, 2011
By 
This review is from: Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) (Paperback)
Vine Customer Review of Free Product (What's this?)
Context for this review: I am a data miner with 20 years experience, and own the first edition of this book.

Good:
- Accessible writing style
- Broad coverage of algorithms and data mining issues, with an eye toward practical issues
- Needless technical trivia (derivations and the like) are avoided
- Algorithms are completely spelled out: A competent programmer should be able to turn these descriptions into functioning code.
- Third edition makes meaningful improvements on previous editions

Bad(ish):
- Approximately one-third of this book is now devoted to the WEKA data mining software. I have nothing against WEKA, and it is a good choice for a text such as this, since WEKA is free. In my opinion, though, this coverage consumes too many pages of this book.
- Data mining draws from a number of fields with separate roots (statistics, machine learning, pattern recognition, engineering, etc.), and many techniques go by multiple names. As with many other data mining books, this one does not always point out the aliases by which data mining methods are known.

The bottom line: This is still the best data mining text on the market.
Help other customers find the most helpful reviews 
Was this review helpful to you? Yes No


28 of 29 people found the following review helpful
4.0 out of 5 stars Applying Machine Learning to Data Mining problems, April 1, 2011
By 
owookiee "owookiee" (Winston-Salem, NC USA) - See all my reviews
(VINE VOICE)   
This review is from: Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) (Paperback)
Vine Customer Review of Free Product (What's this?)
The subtitle of the book should really be emphasized more: Practical Machine Learning Tools and Techniques. This isn't a book about adhoc SQL queries and database statistics, it is about tools to discover relationships you didn't know you were looking for. Much of the book shows how to handle knowledge formation and representation, statistical modeling and projections. The one critique I have in regard is that much of the algorithm breakdowns are done in prose rather than true pseudocode.

I would like to echo other reviews that point out the text focuses on WEKA, and the authors indicate this is by intent. Though they do give much generic information, at some point you have to pick a horse to hitch your carriage to, and an established open-source project in Java is probably most widely accessible. Their coverage of WEKA claims 50% more features than the 2nd ed. and indeed it consumes half the book. I feel this is a good thing, as it lends great practicality to the book, allowing you to dig right in and get something actually done.

There are some additions to the 3rd ed. that modernize the book a bit. Showing how data can be reidentified (and the ethical implications) is pertinent to today's HIPAA-regulated medical environments. They also touch on web and ubiquitous mining, reflecting our growing foray into non-traditional cloud sources of information.
Help other customers find the most helpful reviews 
Was this review helpful to you? Yes No


21 of 21 people found the following review helpful
5.0 out of 5 stars My favorite practical machine learning book, September 3, 2011
This review is from: Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) (Paperback)
Vine Customer Review of Free Product (What's this?)
There exists a couple of classics of Machine learning, with various strengths and weaknesses. "The elements of statistical learning" by Hastie and company. Bishop's book, "Pattern Recognition and Machine Learning." And now, this book, "Data Mining." I'd say this is the most practical of the three books. The other two I mentioned are oriented towards theoretical underpinnings, and cataloging the rich zoology of machine learning techniques. This one tells you how to get stuff done. Lots of practical ideas on discretization, denoising, data preparation and performance characterization. It even has practical advice on things you really need an expert opinion on: for example, when using data folding techniques for cross validation ... what is a good number of folds to use? This book will tell you. It's like having a couple of seasoned experts looking over your shoulder when you're trying to get things done. It had a detailed recipe in it for something I really needed to solve... and their recipe worked!
While the subject matter is similar to the Bishop and Hastie books: what this most reminded me of was the classic physics text, "Numerical recipes." It's all very well having a good theoretical understanding of the techniques you're using. It's vastly more important to have advice on using them properly. This is that book; uniquely so, thus far, in my experience.
It's also a brilliant manual for their Weka machine learning environment, which is incredibly useful. I don't use the Weka UI, but I have called upon Weka as a library extension to the R programming environment. Mostly because of this book: it's both a recipe book and a map to a large collection of recipes you can use to solve your machine learning problems.

There isn't so much on time series applications, sadly, which is something I end up working with a lot. I'd love to see an extended chapter on the particular difficulties in using machine learning techniques to mine and forecast time series.
Help other customers find the most helpful reviews 
Was this review helpful to you? Yes No


28 of 30 people found the following review helpful
4.0 out of 5 stars Mixed Opinion, April 27, 2011
By 
GX (United States) - See all my reviews
(VINE VOICE)   
This review is from: Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) (Paperback)
Vine Customer Review of Free Product (What's this?)
Fantastic book if you need to use WEKA; probably the best recommendation available.

If, however, you're not going to be using WEKA then the book is still valuable, but I challenge the true 'practicality' of it. The content is thorough but perhaps more academically oriented than as industry focused as I would have liked. The author keeps it very accessible, particularly as far as mathematics and statistics go. While this might make the book a little more long winded - in my view it makes it a far easier to get into the groove and allows you to read it like a book.

* Highly recommended for WEKA users
* For others users I suggest you look through to see if it will really be helpful before plunking down the cash
Help other customers find the most helpful reviews 
Was this review helpful to you? Yes No


6 of 6 people found the following review helpful
4.0 out of 5 stars Concept over code, May 16, 2011
This review is from: Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) (Paperback)
Vine Customer Review of Free Product (What's this?)
If you are looking for a simple how-to book that gives you a lot of sample source code, this is not for you. If you want to learn the concepts and theoretical underpinnings of various algorithms and techniques, this is a great place to start. The authors clearly stress the concepts of data mining that can be applied to a variety of specific applications. This is a must have volume for anyone wanting to truly understand the theories and concepts behind the various approaches to data mining and the tradeoffs involved with each approach. Those with a background in artificial intelligence will have an easier time getting through this material but such a background is not necessary to gain a solid foundation in the topics. It is well written and organized for self-study. But it may be a little intimidating for some beginners.
Help other customers find the most helpful reviews 
Was this review helpful to you? Yes No


7 of 8 people found the following review helpful
5.0 out of 5 stars Dense, but thorough, introduction to data mining theory and practice., October 9, 2011
This review is from: Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) (Paperback)
Vine Customer Review of Free Product (What's this?)
This is an excellent text. The authors early on define data mining "as the extraction of implicit, previously unknown and potentially useful information from data". The techniques, methodologies and algorithms for achieving this goal are the crux of this 600 page text.

It is grindingly thorough and only the dedicated will make it through the book without assistance. For those who do, they will have an excellent grounding in the theory and basic techniques of machine learning. The last third of the book introduces WEKA. WEKA is an open-source workbench that permits the data mining student to try out all the algorithms presented in the book. It's free and extensible.

The reader needs a pretty good grasp of database technology and more than a little knowledge of statistics and math. The latter is not an absolute necessity, but will make comprehending this material a faster and easier process.

The authors, collectively, has a clear writing style, free of academic cant. If you take it slow, everything ultimately becomes clear.

Overall, this is an excellent introduction to data mining, but is not for those who expect to learn even the fundamentals of this technology in a few days time. That just ain't gonna happen.

Jerry
Help other customers find the most helpful reviews 
Was this review helpful to you? Yes No


4 of 4 people found the following review helpful
4.0 out of 5 stars good textbook to start machine learning / data mining, December 13, 2011
Verified Purchase(What's this?)
This review is from: Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) (Paperback)
The book is really good to start learning machine learning and data mining.

Pros
- It doesn't jump into algorithms with mathematical details. It starts with what is it all about, what input and output look like in typical machine learning problems.
- One point that I really liked is that the book gives algorithms in two chapters (chapter 4 and 6). The first chapter is about basics and latter one gives detail about these algorithms.
- It also covers well that I think it is mostly ignored by other books/tutorials: practical issues. How to normalize data, what happens your data have both categorical and numerical features, discretizing numerical features and so on.
- If you consider using Weka, you should have this book. Authors are from the team who built Weka. For each algorithm described in the book, corresponding names of implementations in Weka are given too. With the book it is easier to understand parameters of Weka implementations of algorithms. Also last part of the book is like extensive Weka tutorial.

Cons
- In a few points, the book contains unnecessary details, although it is not the case for overall of the book. One of such things that I remember is chapter 4.7. The book spends 5 whole pages to how to find nearest neighbor efficiently (not-easy stuff), which I think it is really implementation detail. Instead of it, it could explain what nearest neighbor is, or something else.
- The part about Weka has several figures, mostly Weka screen shots. It was difficult to follow these figures, because of black-white screen shots. I think these figures should be in color in the next edition, which will make much easier to follow.
Help other customers find the most helpful reviews 
Was this review helpful to you? Yes No


4 of 4 people found the following review helpful
5.0 out of 5 stars You will need some time but it is worth the investment, December 11, 2011
Verified Purchase(What's this?)
This review is from: Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) (Paperback)
To get the most out of this book, you need to either be a statistician, AI professional, or be willing to invest some time. But: if you commit yourself, then this book goes a long way to substitute for a graduate-level course on data mining. Don't get me wrong - it is not written with an academic audience in mind; as a matter of fact, it is unusually rich with application examples. But there is a lot to digest conceptually and many of the examples are quite involved. As such, it addresses the opposite end the O'Reilly series of how-to books. This one gets you up to speed with one of if not the best software package for data mining in all its many facets. With Weka and 'R', you have the tools to tackle many of the World's problems, and this book is the best introduction to one part of the duo.
Help other customers find the most helpful reviews 
Was this review helpful to you? Yes No


10 of 13 people found the following review helpful
1.0 out of 5 stars Avoid, July 7, 2013
By 
Charles (Philadelphia, PA) - See all my reviews
Verified Purchase(What's this?)
This review is from: Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) (Paperback)
Reading some of these reviews I feel like I must have gotten another book. I really didn't think the book was worth the time or money investment.

My main issues were:
1. 50% of the book covers WEKA
- but who is really going to use WEKA over a product like R.
- the WEKA coverage is mind numbingly bad. Lists of algorithm names without explanations of those algorithms and no real practical advice or examples using the program.
2. The 50% of the book that covers general data mining is not really that good at all. It is meant to be an easily accessible overview without technical details but manages to be so breezy an overview as to be totally useless.
3. The "Data Mining with Rattle and R" (as a practical introduction) is so much better in almost every area that I can't understand why people are still recommending this book.
Help other customers find the most helpful reviews 
Was this review helpful to you? Yes No


3 of 3 people found the following review helpful
2.0 out of 5 stars Difficult to read with lots of references, May 14, 2013
This review is from: Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems) (Paperback)
I wish the chapters are easy to read. The earlier chapters starts by referencing a section in the future and later chapters refer to earlier stuff. It is almost impossible to read a sentence without referring back and forth to a section diagram etc.
For ex. Chapter 4 refers to section in chapter 6 and chapter 6 sections starts, " as we discussed in section 4.2 ...".
Also all figures and tables are out of place and requires scrolling back and forth.
Most description could be self contained in one figure.
Help other customers find the most helpful reviews 
Was this review helpful to you? Yes No


‹ Previous | 1 2 3 4 | Next ›
Most Helpful First | Newest First

Details

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition (The Morgan Kaufmann Series in Data Management Systems)
$69.95 $44.01
Usually ships in 2 to 3 weeks
Add to cart Add to wishlist
Search these reviews only
Rate and Discover Movies
Send us feedback How can we make Amazon Customer Reviews better for you? Let us know here.