Data Mining with Decision Trees

Theory and Applications

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

Contents:

• Introduction to Decision Trees
• Training Decision Trees
• A Generic Algorithm for Top-Down Induction of Decision Trees
• Evaluation of Classification Trees
• Splitting Criteria
• Pruning Trees
• Popular Decision Trees Induction Algorithms
• Beyond Classification Tasks
• Decision Forests
• A Walk-through Guide for Using Decision Trees Software
• Advanced Decision Trees
• Cost-sensitive Active and Proactive Learning of Decision Trees
• Feature Selection
• Fuzzy Decision Trees
• Hybridization of Decision Trees with Other Techniques
• Decision Trees and Recommender Systems

Readership: Researchers, graduate and undergraduate students in information systems, engineering, computer science, statistics and management.