A Novel Algorithm for Classification Rule Discovery based on Concept

This study established concept elements based on granular computing theory and the isomorphic relation between rated scales in formal concept analysis (FCA) and constructed the correlation of the concept elements. A concept granule was constructed by studying the mapping relation between concept elements. The common polymerization and extension forms of the concept granule were given. We studied the condition in which the granular structure in a conceptual system is purified, as well as the formation mechanism and generalized cohesiveness of concept granules. An algorithm for classification rule discovery algorithm based on concept granule structure—the GRD algorithm—was created. According to experimental results, the proposed GRD algorithm has higher classification accuracy, simpler rule set, and better generalization than traditional algorithms for classification rule discovery. The formal representation based on conceptual elements shows that a knowledge representation model that is complete in terms of semantic description can be built. Subject Categories and Descriptors H.2.8 [Database Applications]: Data mining; I.2.4 [Knowledge Representation Formalisms and Methods]: Representations (procedural and rule-based) General Terms: Classification, Rule mining, Formal Concept Analysis