Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems) | 
 enlarge | Authors: Ian H. Witten, Eibe Frank
Publisher: Morgan Kaufmann
Category: Book
List Price: $65.95
Buy New: $41.42
You Save: $24.53 (37%)
New (38) Used (17) from $33.00
Rating:  25 reviews
Sales Rank: 10060
Media: Paperback
Edition: 2
Pages: 560
Number Of Items: 1
Shipping Weight (lbs): 2.6
Dimensions (in): 9.1 x 7.5 x 1.2
ISBN: 0120884070
Dewey Decimal Number: 006.3
EAN: 9780120884070
Publication Date: June 8, 2005
Availability: Usually ships in 1-2 business days
Shipping: International shipping available
Condition: Brand New, Perfect Condition, Please allow 4-14 business days for delivery. 100% Money Back Guarantee, Over 1,000,000 customers served.
| |
| Similar Items:
|
| Editorial Reviews:
Product Description
As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work.
The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.
* Algorithmic methods at the heart of successful data miningincluding tried and true techniques as well as leading edge methods
* Performance improvement techniques that work by transforming the input or output
* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualizationin a new, interactive interface
Download Description
Like the popular first edition, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining-including both tried-and-true techniques of the past and Java-based methods at the leading edge of contemporary research. If you're involved at any level in the work of extracting usable knowledge from large collections of data, this clearly written and effectively illustrated book will prove an invaluable resource. Complementing the authors' instruction, including a fully-revised Chapter 8 and 30 new technique sections, is a fully functional platform-independent Java software s
|
| Customer Reviews: Read 20 more reviews...
 Great Book in Every Way November 1, 2005
R. Williams (Los Angeles, CA United States)
19 out of 25 found this review helpful
The first edition of this book was good, but this is a huge improvement. The writing is really great, very clear, even when it heads into deeper waters. The explanation, for instance, of the various algorithms for accomplishing attribute discretization is very clear, even as the equations start to get very long and complicated.
It's pretty incredible that this book is so readable, kudos to the authors for that. Most importantly, though, it gives you a very good sense of what you need to know as you work through the many data mining options. The authors' assertion that DM is not a magic box is good, and it is clearly a dictate that they mind themselves throughout the book: DM doesn't mean that you just plug in a black box and it starts to lay eggs. Generating rules, building trees and knowing how to pick attributes to build the tree from are all critical topics that get excellent treatment.
Good Book for Data Mining August 31, 2005
N. Sanders (Nashville, TN USA)
17 out of 28 found this review helpful
This is the second edition of the author's Data Mining book. The first part of the book focuses on data mining algorithms, implementation issues, and how to evaluate the results of the data mining model. The second part focuses on the authors "Weka Machine Learning Workbench" which is available under a GNU General Public License. See their web site: http://www.cs.waikato.ac.nz/~ml/weka/index.html for the software. This software appears to be widely used at academic institutions.
The first section of the book provides an overview of the algorithms that the software implements. If you need an in depth understanding of the algorithms, you will need additional information sources. If you simply download the software without an understanding of which algorithms are appropriate to your data mining problem, you may become frustrated with the performance, or, even worse, you may misinterpret the results of the data mining model.
In general, learning data mining is much more complex than this book (or any other single book) can adequately describe; however, this is an excellent source for someone interested in data mining.
Incredibly practical introduction October 30, 2006
David Donohue (Wilmington, Delaware, USA)
11 out of 12 found this review helpful
This book is perfect if you are trying to get your hands around what data mining and machine learning is. Most of the books I have read on this subject want to start with equations and get more complex from there, with little practicality. This book makes extensive use of examples and introduces the mathematical basis for algorithms where needed. The authors make the point that simpler algoritms often work best for solving machine learning problems. Similarly, I would argue, simpler books work best for understanding highly complex fields. I very highly recommend this book.
Lucid March 21, 2006
Developer (Brooklyn, NY United States)
19 out of 22 found this review helpful
I'm surprisingly please with this book. I've been reading up on the topic and associated algorithms in other books for some time; I'm a software developer but don't have a statistics background, and so felt a lot of the texts were too focused on the math and the theory while being thin on content when it came to "rubber hitting the road", or even using clear, simple examples and straight-forward notation.
This book is so well-written that it communicates the concepts clearly, lucidly and in an organized fashion. The section that introduces Bayesian probability was drop-dead simple to follow. Quite frankly, having read a few other treatments on it, I can now say that everything else I read before this was overly complicated. Brevity is the soul of wit, no?
To the reviewer who criticized the authors use of words to describe equations: This is what the authors intended to do. Would you fault them for writing in English if you wanted Greek? Not everyone who can benefit from applied data mining has the requisite background to understand the nitty gritty mathematics, nor should they have to, if they just want to understand the behavior and practical applications of the technology.
If you want to use WEKA properly, you need this book. October 8, 2005
L. Glorfeld (Fayetteville AR, USA)
8 out of 14 found this review helpful
This is a very useful improvement ovet the original edition of this book. I actually found this second edition to be useful in informing the reader about the basics of using the GUI interface to WEKA. I have still found some strange results using WEKA. For example, when using the C4.5 algorithm with boosting, I set the upper confidence limit to .05. WEKA built a small original tree and then ignored my upper confidence setting and built all the rest of the trees ignoring my input value which resulted in extensive trees using, I assume, .25 as the upper confidence limit. Anyway, the book is worth the money if you want to get the most out of WEKA and also want a basic understanding of the theory and algorithms used.
|
|
|
