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, the science and technology of exploring large and complex bodies of data in order to discover useful patterns. The area is of great importance because it enables modeling and knowledge extraction from the abundance of data available. Both theoreticians and practitioners are continually seeking techniques to make the process more efficient, cost-effective and accurate. Decision trees, originally implemented in decision theory and statistics, are highly effective tools in other areas such as data mining, text mining, information extraction, machine learning, and pattern recognition. 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
• Able to process datasets that may have errors or missing values
• High predictive performance for a relatively small computational effort
• Available in many data mining packages over a variety of platforms
• Useful for various tasks, such as classification, regression, clustering and feature selection