Abstract
Introduces an incremental, probabilistic rough set approach to rule discovery in very large, complex databases with uncertainty and incompleteness. The approach is based on the combination of generalization distribution table (GDT) and rough set methodology. A GDT is a table in which the probabilistic relationships between concepts and instances over discrete domains are represented. By using a GDT as an hypothesis search space and combining the GDT with the rough set methodology, noises and unseen instances can be handled, biases can be flexibly selected, background knowledge can be used to constrain rule generation, and the rules with strengths can be effectively acquired from very large, complex databases in an incremental, bottom-up mode. We focus on basic concepts and an implementation of our methodology.
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