Data Mining, Second Edition : Concepts and Techniques, Second Edition (The Morgan Kaufmann Series in Data Management Systems)
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| Data Mining, Second Edition : Concepts and Techniques, Second Edition (The Morgan Kaufmann Series in Data Management Systems) | |||||||||||||||||||||||||||||
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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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| 06-22-08 | 4 | (NA) |
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This book is a good introduction on Data Mining with solid explanations of the mathematics behind the methods.
(Review Data Last Updated: 2008-09-03 04:42:26 EST)
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| 12-08-06 | 4 | 7\7 |
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This is a great textbook for an undergraduate or layperson to the information sciences, but specialists may find it lacking depth. It is very good at identifying practices and principles that would guide a high-level planner toward a sound research program. That said, this book exhaustively covers the breadth of the modern field at the expense of formulas, algorithms, and source code that would have been valuable to an engineer or scientist with plans to implement.
* Buy this book if you require a high-level understanding of the concepts and techniques used in the field. * Don't buy this book if you are planning to specialize in data mining, or if you have plans to implement yourself. (Review Data Last Updated: 2007-09-07 19:54:08 EST)
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| 12-08-06 | 4 | 12\12 |
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This is a great textbook for an undergraduate or layperson to the information sciences, but specialists may find it lacking depth. It is very good at identifying practices and principles that would guide a high-level planner toward a sound research program. That said, this book exhaustively covers the breadth of the modern field at the expense of formulas, algorithms, and source code that would have been valuable to an engineer or scientist with plans to implement.
* Buy this book if you require a high-level understanding of the concepts and techniques used in the field. * Don't buy this book if you are planning to specialize in data mining, or if you have plans to implement yourself. (Review Data Last Updated: 2008-03-16 01:34:43 EST)
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| 02-14-06 | 1 | 1\17 |
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Do yourself a favor and stay away from this book. The book is put together by gathering some data mining concepts padded with tons of buzzwords to "teach" you about data mining. It really fails to teach you anything and it really succeeds to confuse the hell out of the reader. The language used on this book is the poorest I have ever seen in my entire life. The people how wrote this book and the publishers who published it should be ashamed of themselves. It is a shame that I had to pay for this piece of work because I have to read it for a course I am taking and I really dread opening it every week.
(Review Data Last Updated: 2006-09-06 08:48:12 EST)
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