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

• Published in: Pattern recognition letters Vol. 106; pp. 61 - 68
• Main Authors: Suo, Mingliang, An, Ruoming, Zhou, Ding, Li, Shunli
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 15-04-2018; Elsevier Science Ltd
• Subjects: Algorithms; Clustering; Clusters; Data management; Data mining; Data processing; Datasets; Distributed processing; Fast reduction; Grid cluster; Learning; Learning theory; Machine learning; Reduction; Rough self-learning; Rough set; Rough set models; Self; Set theory; Universe; Grid cluster; 41A10; Rough self-learning; 65D05; Fast reduction; 65D17; 41A05; Rough set
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2018.02.018

•A grid cluster algorithm is presented along with low time complexity.•The cluster algorithm could select cluster centers automatically.•A novel rough set model is proposed based on the cluster algorithm.•Rough self-learning theory is raised.•The new rough set model has the abilities of rough self-learning and fast reduction.
Rough set theory has been playing a significant role in data mining, and its progress to intelligentization in future requires more abilities, such as hybrid data processing, fast attribute reduction and self-learning. The essential demand of the three abilities is the knowledge depicting of the considered universe. Grid subspace cluster(GSC) algorithm characterized by densities and distances in grid subspace is presented along with the automatically selecting of cluster centers, which is regarded as the knowledge depiction in rough set model, i.e. grid-clustered rough set(GCRS) model. For the ability of self-learning, rough self-learning theory including extensional learning and intensional learning is raised. Subsequently, a fast attribute reduction algorithm and a rough self-learning algorithm based on GSC, rough self-learning theory and GCRS model are designed. A multitude of experiments substantiate that, GCRS model could meet the future demands of rough set theory. [Display omitted]
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.