Title: A logical formulation of the granular data model
Author: Tuan-Fang Fan, Churn-Jung Liau, Tsau-Young Lin, Karen Lee
Department of Computer Science and Information Engineering, National Penghu University, Penghu 880, Taiwan.
Institute of Information Science, Academia Sinica, Taipei 115, Taiwan.
Department of Computer Science, San Jose State University, San Jose, CA 95192, USA.
Department of Computer Science, San Jose State University, San Jose, CA 95192, USA
Abstract: In data mining problems, data is usually provided in the form of data tables. To represent knowledge discovered from data tables, decision logic (DL) is proposed in rough set theory. While DL is an instance of propositional logic, we can also describe data tables by other logical formalisms. In this paper, we use a kind of many-sorted logic, called attribute value-sorted logic, to study association rule mining from the perspective of granular computing. By using a logical formulation, it is easy to show that patterns are properties of classes of isomorphic data tables. We also show that a granular data model can act as a canonical model of a class of isomorphic data tables. Consequently, association rule mining can be restricted to such granular data models.
Keywords: isomorphic data tables; rough sets; decision logic; first-order logic; granular data models; data mining; rough set theory; association rule mining.
Int. J. of Granular Computing, Rough Sets and Intelligent Systems, 2010 Vol.1, No.3, pp.289 - 307
Available online: 30 Nov 2009