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Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced.
This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in. New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition.
This book invites readers to explore the many benefits in data mining that decision trees offer:
- Self-explanatory and easy to follow when compacted
- Able to handle a variety of input data: nominal, numeric and textual
- Scales well to big data
- Able to process datasets that may have errors or missing values
- High predictive performance for a relatively small computational effort
- Available in many open source data mining packages over a variety of platforms
- Useful for various tasks, such as classification, regression, clustering and feature selection
Readership: Researchers, graduate and undergraduate students in information systems, engineering, computer science, statistics and management.
- Sales Rank: #2035606 in Books
- Published on: 2014-10-23
- Released on: 2014-09-03
- Original language: English
- Number of items: 1
- Dimensions: 9.00" h x .74" w x 6.00" l, .0 pounds
- Binding: Hardcover
- 328 pages
From the Inside Flap
Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced.
This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in. New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition.
This book invites readers to explore the many benefits in data mining that decision trees offer:
Self-explanatory and easy to follow when compacted
Able to handle a variety of input data: nominal, numeric and textual
Scales well to big data
Able to process datasets that may have errors or missing values
High predictive performance for a relatively small computational effort
Available in many open source data mining packages over a variety of platforms
Useful for various tasks, such as classification, regression, clustering and feature selection
About the Author
Lior Rokach is an Associate Professor of Information Systems and Software Engineering at Ben Gurion University of the Negev. Dr Rokach is a recognized expert in intelligent information systems and has held several leading positions in this field. His main areas of interest are Machine Learning, Information Security, Recommender Systems and Information Retrieval. Dr Rokach is the author of over 100 peer reviewed papers in leading journals conference proceedings, patents, and book chapters. In addition, he has also authored six books in the field of data mining.
Professor Oded Maimon from Tel Aviv University, previously at MIT, is also the Oracle chair professor. His research interests are in data mining and knowledge discovery and robotics. He has published over 300 papers and ten books. Currently he is exploring new concepts of core data mining methods, as well as investigating artificial and biological data.
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