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Title: Introduction to Data Mining
ISBN: 0321321367
Author:
Pang-Ning Tan
Michael Steinbach
Vipin Kumar
Publicate Date: 2005-05-12
Publish: 2005-05-12
List Price: $97.00
Average Customer Rating: 5.0
Format: Hardcover
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Amazon Lowest New Price: $77.50
Amazon Lowest Used Price: $61.84
Amazon Merchant Price: $77.50
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| Customer Review: |
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1: Great Machine Learning Introduction
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.
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2: As an introduction, I love this book
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.
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3: very good introduction
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.
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4: Amazingly well written: simple, to the point, easy to read, and full useful information
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.
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5: More than just about data mining
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.
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