Covering with Reducts - A Fast Algorithm for Rule Generation

In a rough set approach to knowledge discovery problems, a set of rules is generated basing on training data using a notion of reduct. Because a problem of finding short reducts is NP-hard, we have to use several approximation techniques. A covering approach to the problem of generating rules based on information system is presented in this article. A new, efficient algorithm for finding local reducts for each object in data table is described, as well as its parallelization and some optimization notes. A problem of working with tolerances in our algorithm is discussed. Some experimental results generated on large data tables (concerned with real applications) are presented.