Ning Zhong

An incremental, probabilistic rough set approach to rule discovery

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.

Data Mining: A Probabilistic Rough Set Approach

This paper introduces a new approach for mining if-then rules in databases with uncertainty and incompleteness. The approach is based on the combination of Generalization Distribution Table (GDT) and the 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 a 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 if-then rules with strengths can be effectively acquired from large, complex databases in an incremental, bottom-up mode. In this paper, we focus on basic concepts and an implementation of our methodology.

Frameworks for Mining Binary Relations in Data

This paper extends the notion of information tables and concept hierarchies of equivalence relations to binary relations. So extended rough set theory and attribute oriented generalization techniques can be used to mining binary relations in data.