Abstract
In this tutorial, we survey recent developments in learning tree-based models for classification and regression called predictor trees. The tutorial has three parts: (1) A general overview of tree-based classification and regression. (2) A survey of methods to construct predictor trees. (3) An overview of scalable data access methods to construct predictor trees from very large training databases.
In the first part, we motivate predictor trees and their use in a data mining environment. We show results from real-life studies that illustrate how predictor trees give understandable models where traditional models are hard or counter-intuitive to interpret, and compare related methods for classification and regression.
In the second part of the tutorial, we discuss choices involved in tree construction, including different split selection methods and tree pruning. Although we survey the most popular methods, including work from all KDD sub-communities, we emphasize recent work from the statistics literature. Wherever possible, we interleave results on real datasets. The methods presented in this part assume that the complete training database fits into main memory.
The third part of the tutorial covers scalable methods for predictor tree construction. We first motivate the concept of scalability and then survey recent work in the database literature on scalable data access methods for constructing predictor trees from very large training databases.

Abstract
In recent years we witness a rapid growth of interest in rough set theory and its applications, worldwide. The theory has been followed by the development of several software systems that implement rough set operations, in particular for solving knowledge discovery and data mining tasks. Rough sets are applied in domains, such as, for instance, medicine, finance, telecommunication, vibration analysis, conflict resolution, intelligent agents, pattern recognition, control theory, signal analysis, process industry, marketing, etc. The tutorial introduces basic notions and discusses methodologies for analyzing data and surveys some of applications. In particular it presents applications of rough set methods for feature selection, feature extraction, discovery of patterns and their applications for decomposition of large data tables as well as the relationship of rough sets with association rules. Boolean reasoning is crucial for all the discussed methods. The tutorial also presents an overview of some extensions of the classical rough set approach like rough mereology developed as a tool for synthesis of objects satisfying a given specification in a satisfactory degree with potential applications in such areas like granular computing, spatial reasoning or data mining in distributed environment. Survey of recent advances in granular computing is included. The tutorial discusses applications of rough set methods for knowledge discovery and data mining in medical databases.