Mining positive and negative rules via one-sided fuzzy three-way concept lattices

Abstract

Mining association rules are a major area of research in data mining, as there are typically a large number of rules in each data set. And extracting necessary rules from each data set requires efficiency and careful consideration. In this paper, we propose a novel mining algorithm for efficiently acquiring positive and negative quantitative association rules (positive and negative QA-rules) in a fuzzy or multi-valued ordered context based on a three-way concept lattice (3CL). To this end, a new 3CL, called a one-sided fuzzy three-way concept lattice (OF3WC-lattice), is constructed based on a fuzzy formal context (FFcontext). The designed OF3WC-lattice describes the dependencies between objects and attributes, whether in the original FFcontext or its complement context. And the OF3WC-lattice is based on two kinds of two-way operators to make it consistent with practical linguistics. In addition, we propose an algorithm for constructing an OF3WC-lattice through analyzing relationships between the novel concept lattice and traditional one-sided fuzzy concept lattices. Experimental results demonstrate that our method effectively mines positive and negative QA-rules.