Application of Covering Rough Sets to Linguistic Dynamic Systems

Rough set theory is a technique of granular computing. As a generalization of classical rough set theory, covering-based rough set has been used for attribute reduction in data mining. Linguistic dynamic systems are dynamic processes involving mainly computing with words instead of numbers for modeling and analysis of complex systems and human-machine interfaces. They are all potential methods for granular computing. This paper proposes an outline to combine these two techniques into a better and unified granular computing methodology. We present research issues and challenges for this exploration.