|
| Introduction to Data Mining | 
 enlarge | Authors: Pang-ning Tan, Michael Steinbach, Vipin Kumar
Publisher: Addison Wesley
Category: Book
List Price: $97.00
Buy New: $52.99
You Save: $44.01 (45%)
New (22) Used (11) from $52.99
Avg. Customer Rating:  10 reviews
Sales Rank: 123964
Media: Hardcover
Edition: US ed
Number Of Items: 1
Pages: 769
Shipping Weight (lbs): 3.1
Dimensions (in): 9.3 x 7.8 x 1.3
ISBN: 0321321367
Dewey Decimal Number: 006.312
EAN: 9780321321367
ASIN: 0321321367
Publication Date: May 12, 2005
Availability: Usually ships in 1-2 business days
Condition: Chinese International Edition. Paperback not Hardback. Same content as US edition but lower paper quality.
|
| Editorial Reviews:
Product Description
Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms.
|
| Customer Reviews: Read 5 more reviews...
Great Machine Learning Introduction June 24, 2008
This is the book I used to introduce myself to data mining as graduate student in computer science. At the time I thought it was a very well organized and self contained book. Since then I have been studying machine learning full time and I still use this book to get really great explanations of key algorithms and concepts. Every time I go back to this book I'm surprised to find all sorts of topics covered that I hadn't noticed on previous readings. Basically it covers a good breadth of topics in an easy to understand manner. I highly recommend this book for anyone interested in data mining or machine learning.
As an introduction, I love this book June 22, 2008
If you think you are interested in Data Mining this is a great place to start. This book would work well for people interested in self study, or someone who is considering going to grad school to pursue a field utilizing data mining, or doing data mining research directly.
The book covers the core data mining concepts, with clear examples on how the concepts could be applied to toy problems. The book is light on math and heavy on application, which is great at maintaining interest. This book is not commonly used as a course textbook at the grad level because of its shallow treatment of the underlying math.
Sometimes you just want to know how, and worry about why later.
Also, if you think data mining might be of use to your research or your professional work, this book provides a broad overview of topics. If you are unfamiliar with data mining, and have just heard the term, running through the introductions of each chapter will quickly point you to techniques that will be most useful to you.
 very good introduction May 19, 2008
1 out of 1 found this review helpful
I agree with the other reviews: The book is amazingly well-written, and the two chapters on cluster analysis are second-to-none;
Though I am particularly enthusiastic about this book, I believe that it cannot deserve 5 stars, for the following reasons:
- Kernel methods: like most books on this subject, the authors do not explain how to choose the most appropriate kernel(s)
- Cluster analysis: No examples of time-series
- Fully worked-out real-world examples are missing
- no solutions to the exercises
If not possible to wait for a second edition, do not hesitate, this is definitely the best introduction you can find.
Amazingly well written: simple, to the point, easy to read, and full useful information October 30, 2007
4 out of 4 found this review helpful
This book is amazingly well written. Everything is explained in a very clear and to-the-point style. The book can be read from front to back or used as a reference book. It contains countless diagrams and the structure of the content is immediately apparent.
The book covers a lot of the important aspects of data mining. It provides algorithms and techniques for classification, clustering, association analysis, and anomaly detection. Every algorithm is not only formally stated, but also explained in a way that conveys intuition.
I only wish other authors also wrote books this way.
More than just about data mining March 9, 2007
2 out of 3 found this review helpful
This book gives an excellent overview of data mining techniques, and gives thorough information about machine learning fundamentals. The key advantages of this book are its clean structure and high quality content and illustrations.
|
|
|
| | New Releases | | • | Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications (Data-Centric Systems and Applications) | | • | Master Data Management (The MK/OMG Press) | | • | Netezza Underground: The unauthorized tales of derring-do and adventures in resilient data warehousing solutions. | | • | SQL in a Nutshell (In a Nutshell (O'Reilly)) | | • | Master Data Management | | • | Data Warehouse 100 Success Secrets - 100 most Asked questions on Data Warehouse Design, Projects, Business Intelligence, Architecture, Software and Mo | | • | The Data Model Resource Book: Universal Patterns for Data Modeling | | • | Database Archiving: How to Keep Lots of Data for a Very Long Time (The MK/OMG Press) (The MK/OMG Press) | | • | Information Systems Security: 4th International Conference, ICISS 2008, Hyderabad, India, December 16-20, 2008, Proceedings | | • | New Trends in Data Warehousing and Data Analysis (Annals of Information Systems) |
|
|
|
|
