Affiliations: College of Information Science, Drexel University,
Philadelphia, PA 19104, USA | Department of Computer Science, San Jose State
University, San Jose, CA 94403, USA | Department of Computer Science, California State
University Dominguez Hills, Carson, CA 90747, USA
Abstract: Rough sets theory was proposed by Pawlak in the early 1980s
and has been applied successfully in a lot of domains. One of the major
limitations of the traditional rough sets model in the real applications is the
inefficiency in the computation of core and reduct, because all the intensive
computational operations are performed in flat files. In order to improve the
efficiency of computing core attributes and reducts, many novel approaches have
been developed, some of which attempt to integrate database technologies. In
this paper, we propose a new rough sets model and redefine the core attributes
and reducts based on relational algebra to take advantages of the very
efficient set-oriented database operations. With this new model and our new
definitions, we present two new algorithms to calculate core attributes and
reducts for feature selections. Since relational algebra operations have been
efficiently implemented in most widely-used database systems, the algorithms
presented in this paper can be extensively applied to these database systems
and adapted to a wide range of real-life applications with very large data
sets. Compared with the traditional rough set models, our model is very
efficient and scalable.
