Grid-clustered rough set model for self-learning and fast reduction

Rough set theory has been playing a significant role in data mining, but deficiencies still exist in fast attribute reduction and self-learning for decision systems. Grid-clustered rough set (GCRS) model computing the universe partition based on grid subspace cluster (GSC) algorithm is constructed in this paper to fill the aforementioned gaps. GSC algorithm characterized by densities and distances in grid subspace is firstly presented along with the automatically selecting of cluster centers. Then a rough self-learning theory including extensional learning and intensional learning is raised. To realize quick attribute reduction and self-learning, a novel rough set model named GCRS that integrates rough set theory and GSC algorithm is proposed, and a corresponding quick attribute reduction algorithm and a rough self-learning algorithm based on GCRS are subsequently designed. A multitude of experiments demonstrate three aspects: a) results of attribute reduction using GCRS model are proved to be of higher classification accuracies; b) attribute reduction based on GCRS model for big data manifests faster speed; c) results of rough self-learning experiments illustrate the validity of the proposed theory.