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Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) | 
| Authors: Jiawei Han, Micheline Kamber
Publisher: Morgan Kaufmann
Category: Book
List Price: $67.95
Buy Used: $27.50
You Save: $40.45 (60%)
New (8) Used (15) from $27.50
Avg. Customer Rating:  29 reviews
Sales Rank: 660635
Media: Hardcover
Edition: 1st
Number Of Items: 1
Pages: 500
Shipping Weight (lbs): 2.7
Dimensions (in): 9.4 x 7.5 x 1.2
ISBN: 1558604898
Dewey Decimal Number: 006.3
EAN: 9781558604896
Publication Date: August 2000
Availability: Usually ships in 1-2 business days
Condition: * Item in good condition- Typical Used Book and at a great price! * We carefully inspected this * Great customer service * Satisfaction Guaranteed!
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| Editorial Reviews:
Product Description
Here's the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. Data Mining: Concepts and Techniques equips you with a sound understanding of data mining principles and teaches you proven methods for knowledge discovery in large corporate databases.
Written expressly for database practitioners and professionals, this book begins with a conceptual introduction designed to get you up to speed. This is followed by a comprehensive and state-of-the-art coverage of data mining concepts and techniques. Each chapter functions as a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. Wherever possible, the authors raise and answer questions of utility, feasibility, optimization, and scalability, keeping your eye on the issues that will affect your project's results and your overall success.
Data Mining: Concepts and Techniques is the master reference that practitioners and researchers have long been seeking. It is also the obvious choice for academic and professional classrooms.
Classroom Features Available Online:
- instructor's manual
- course slides (in PowerPoint)
- course supplementary readings
- sample assignments and course projects
* Offers a comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data.
* Organized as a series of stand-alone chapters so you can begin anywhere and immediately apply what you learn.
* Presents dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects.
* Provides in-depth, practical coverage of essential data mining topics, including OLAP and data warehousing, data preprocessing, concept description, association rules, classification and prediction, and cluster analysis.
* Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields.
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| Customer Reviews: Read 24 more reviews...
 Great book for data mining November 11, 2008
I bought this book as a text book for data mining. I found this book give a solid introduction to multiple topics and a ready reference. One thing , I found though was a rather superficial treatment of very specific algorithms and a thorough treatment of general ones . Atleast the most popular specific algorithms can be detailed.
 efficient, if technically a bit shallow October 24, 2008
This is a useful book: it provides the most comprehensive state of the art overview of data-mining technology I know of. The emphasis is on 'overview' however - you can find starting points and intuitions, but you will not be able to to do anything very ambitious just on the basis of the purely technical information here. At one point, the details of how linear classifiers work are swept under the carpet with a faintly crass remark about 'fancy math tricks'. If linear classifiers are 'fancy math tricks', what does that make variational methods for probabilistic data modelling? Note, in fact, that advanced machine learning in general, where fancy math tricks are ubiquitous and unavoidable, is not touched - an interesting implicit distinction.
Further, this is not a book you are likely to read for pleasure, for either the prose or the presentation. If you are not professionally involved, you neither need nor want it.
Nevertheless, given all those reservations, I'm happy to have it on the shelf.
Good introduction on Data Mining June 22, 2008
This book is a good introduction on Data Mining with solid explanations of the mathematics behind the methods.
Augustine Nsang: Data Mining Book Purchase May 6, 2008
0 out of 3 found this review helpful
Very reliable seller! The book arrived in time and in very good condition. Thanks a lot!
 poor explanation, Weak Language March 24, 2008
1 out of 1 found this review helpful
Dr. Han is a leader in Data Mining; but unfortunately this book does not speak for that. The explanation is poor, the language is weak and thus, the book is not at all a good read. The book by Pang-Ning Tan and Kumar is much better.
The only good thing is that the second edition has a comprehensive coverage and contains many recent topics (streaming, social network, etc.) which is not available in other textbooks.
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