Data Mining: Concepts and Techniques (2nd edition) Jiawei Han and Micheline Kamber Morgan Kaufmann Publishers, 2006 Bibliographic Notes for Chapter 6 Classification and Prediction Classification from machine learning, statistics, and pattern recognition perspectives has been described in many books, such as Weiss and Kulikowski WK91, Michie, Spiegelhalter, and Taylor MST94, Russel.
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Spine creases, wear to binding and pages from reading. May contain limited notes, underlining or highlighting that does affect the text. Possible ex library copy, thatâ ll have the markings and stickers associated from the library. Accessories such as CD, codes, toys, may not be included. Bookseller Inventory # 3008160089
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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.
- 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
Book Description: Highly anticipated second edition of the definitive reference on data mining by the top authority.
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