Learning and applications based on rough set theory

Data mining is emerging as a key enabling technology for a variety of scientific, engineering, medical and business applications. Data mining over large data sets can take a prohibitive amount of time due to the time complexity of the algorithm. Drawing inspiration from the technique "dropping condition attributes" in machine learning, we present scalable parallel data mining algorithms. Our methods are illustrated by a decision table KRS (from a Knowledge Representation System).