Spotlight customer reviews:
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Customer Rating: 
Summary: Great book for data mining
Comment: 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.
Customer Rating: 
Summary: efficient, if technically a bit shallow
Comment: 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.
Customer Rating:
Summary: Good introduction on Data Mining
Comment: This book is a good introduction on Data Mining with solid explanations of the mathematics behind the methods.
Customer Rating:
Summary: Augustine Nsang: Data Mining Book Purchase
Comment: Very reliable seller! The book arrived in time and in very good condition. Thanks a lot!
Customer Rating:
Summary: poor explanation, Weak Language
Comment: 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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Editorial Reviews:
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Our ability to generate and collect data has been increasing rapidly. Not only are all of our business, scientific, and government transactions now computerized, but the widespread use of digital cameras, publication tools, and bar codes also generate data. On the collection side, scanned text and image platforms, satellite remote sensing systems, and the World Wide Web have flooded us with a tremendous amount of data. This explosive growth has generated an even more urgent need for new techniques and automated tools that can help us transform this data into useful information and knowledge.
Like the first edition, voted the most popular data mining book by KD Nuggets readers, this book explores concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining methods, systems, and applications. This new edition substantially enhances the first edition, and new chapters have been added to address recent developments on mining complex types of data- including stream data, sequence data, graph structured data, social network data, and multi-relational data.
Whether you are a seasoned professional or a new student of data mining, this book has much to offer you:
* A comprehensive, practical look at the concepts and techniques you need to know to get the most out of real business data.
* Updates that incorporate input from readers, changes in the field, and more material on statistics and machine learning.
* Dozens of algorithms and implementation examples, all in easily understood pseudo-code and suitable for use in real-world, large-scale data mining projects.
* Complete classroom support for instructors at www.mkp.com/datamining2e companion site.
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