Abstract
Rough set theory (RST) contributes a practical mathematic framework for knowledge discovery and uncertainty reasoning. Hash tables, often employed in data mining, are one of important data models in data structure and can fast access an object according to its key. The computation of rough set approximations plays a crucial role as a basic step of knowledge discovery or relevant tasks under RST. The calculation process of approximations, however, is very time-consuming. To address this issue, through integrating generic information system with highly efficient hash table, the concept of hash information systems (HIS) is firstly introduced and some of their properties are explored. Then, A hash-information-system-based method is proposed for the calculation of rough-set approximations. Finally, This study offers an example for illustrating the validity of the presented approach.
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