Skip to main content. About this product. Stock photo. Brand new: lowest price The lowest-priced brand-new, unused, unopened, undamaged item in its original packaging where packaging is applicable. It will differ from the book on sale, which is an International Student Edition. The Book is printed in English and the Book Contents are.
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Those words only printed to discourage U. See details. See all 5 brand new listings. Buy It Now. Add to cart. About this product Product Information This is the third edition of the premier professional reference on the subject of data mining, expanding and updating the previous market leading edition. This was the first and is still the best and most popular of its kind. Combines sound theory with truly practical applications to prepare students for real-world challenges in data mining. Like the first and second editions, Data Mining: Concepts and Techniques, 3rd Edition equips professionals with a sound understanding of data mining principles and teaches proven methods for knowledge discovery in large corporate databases.
The first and second editions also established itself as the market leader for courses in data mining, data analytics, and knowledge discovery. Revisions incorporate input from instructors, changes in the field, and new and important topics such as data warehouse and data cube technology, mining stream data, mining social networks, and mining spatial, multimedia and other complex data.
This book begins with a conceptual introduction followed by a comprehensive and state-of-the-art coverage of concepts and techniques. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data.
Wherever possible, the authors raise and answer questions of utility, feasibility, optimization, and scalability. Additional Product Features Dewey Edition.
Data Mining Practical Machine Learning Tools And Techniques
Some chapters cover basic methods, and others focus on advanced techniques. The structure, along with the didactic presentation, makes the book suitable for both beginners and specialized readers. The Data Mining: Concepts and Techniques shows us how to find useful knowledge in all that data.
Thise 3rd editionThird Edition significantly expands the core chapters on data preprocessing, frequent pattern mining, classification, and clustering. The bookIt also comprehensively covers OLAP and outlier detection, and examines mining networks, complex data types, and important application areas. The book, with its companion website, would make a great textbook for analytics, data mining, and knowledge discovery courses. Overall, it is an excellent book on classic and modern data mining methods alike, and it is ideal not only for teaching, but as a reference book.
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It adds cited material from about , a new section on visualization, and pattern mining with the more recent cluster methods. It's a well-written text, with all of the supporting materials an instructor is likely to want, including Web material support, extensive problem sets, and solution manuals. Though it serves as a data mining text, readers with little experience in the area will find it readable and enlightening.
That being said, readers are expected to have some coding experience, as well as database design and statistics analysis knowledge. Two additional items are worthy of note: the text's bibliography is an excellent reference list for mining research; and the index is very complete, which makes it easy to locate information.
Also, researchers and analysts from other disciplines--for example, epidemiologists, financial analysts, and psychometric researchers--may find the material very useful. Students should have some background in statistics, database systems, and machine learning and some experience programming. Among the topics are getting to know the data, data warehousing and online analytical processing, data cube technology, cluster analysis, detecting outliers, and trends and research frontiers.
Chapter-end exercises are included. The book is organised in 13 substantial chapters, each of which is essentially standalone, but with useful references to the book's coverage of underlying concepts. Thanks in advance for your time. Skip to content. Search for books, journals or webpages All Pages Books Journals. View on ScienceDirect. Hardcover ISBN: Imprint: Morgan Kaufmann. Published Date: 22nd June Page Count: For regional delivery times, please check When will I receive my book?
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Data Mining: Concepts and Techniques
Introduction Publisher Summary 1. Data Preprocessing Publisher Summary 3. Data Cube Technology Publisher Summary 5. Advanced Pattern Mining Publisher Summary 7. Classification: Basic Concepts Publisher Summary 8. Classification: Advanced Methods Publisher Summary 9.
Advanced Cluster Analysis Publisher Summary Outlier Detection Publisher Summary Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data.
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University of Illinois, Urbana Champaign. Simon Fraser University, Burnaby, Canada.