Algorithm of mining association rules based on Quantitative Concept Lattice
Wang De · Journal of Hefei University of Technology · 2002
The Quantitative Concept Lattice (QCL) evolves from concept lattice by introducing equivalent relation to their intensions and substituting the number of instance in extensions for their extensions. Knowledge on the QCL can be clearly discovered and some kinds of rules such as association rules easily mined. In comparison with Apriori algorithm, rules are presented succinctly and visually. Above all, users can mine association rules according to their subjective interests,and by using the algorithm of mining association rules on the QCL,frequent item sets need not be calculated, then the efficiency of mining association rule is improved. Therefore,the presented algorithm is suitable to the mining of association rules in large databases.