The Research On The Model Of Mining Association Rules Based On Quantitative Extended Concept Lattice

Knowledge discovery in databases (KDD) is a rapidly emerging research field relevant to artificial intelligence and database system. Data Mining is the process of mining the interesting, potentially useful, valid and understandable knowledge in data. Association rule mining is an important sub-branch of Data Mining, which describes the potential relationships between attributes and variables in databases.Concept Lattice represents knowledge with the relationships between the intension and the extension of concepts, and the relationships between the generalization and the specialization of concepts, thus it is applied to the description of association rules mining in databases. The Quantitative Extended Concept Lattice (QECL) evolves from concept lattice by introducing equivalence relationships to its intension and quantity to its extension. The paper is presented by the main ideas, the research on the model of association rules mining based on quantitative extended concept lattice.There are original main ideas in the paper zs follows:(1) The main ideas, algorithm and capability performance analysls of the model of association rules mining based on quantitative extended concept lattice and that of association rule mining by interest-weighted have been proposed, Association rule mining by interest-weighted on quantitative extended concept lattice is an algorithm that we choose those items whose interest-weighted are bigger than the interest-weighted threshold, generate QECL, then mine mutually interest-weighted association rules according to user's interests.Compared with Apriori algorithm, the uniform results of association rules have been obtained by the two methods, but association rules mining by interest-weighted on quantitative extended concept lattice has high quality of time complexity, shows association rules more brief and visual, reduces much searching space and computation of the algorithm, then improves the efficiency and veracityof association rules mining.(2) Traditional marked-basket analysis has been improved, Since it only cares for that the customer have bought something or not, ignores the quantity of those bought, There are some more limitations in practical application. In the paper, I am concerned about both cases, then introduce the idea of interest-weighted to marked-basket analysis, put forward the algorithm how to acquire the interest-weighted threshold, therefore, The association rules mining by interest-weighted on quantitative extended concept lattice is more practical.