ARCA: An Algorithm for Mining Association Rules Based Concept Lattice

Association rule discovery is one of kernel tasks of data mining. Concept lattice, induced from a binary relation between objects and features, is a very useful formal analysis tool. It represents the unification of concept intension and extension. It reflects the association between objects and features, and the relationship of generalization and specialization among concepts. There is a one-to-one correspondence between concept intensions and closed frequent itemsets. This paper presents an efficient algorithm for mining association rules based concept lattice called Area (Association Rule based Concept lAttice). Area algorithm uses concept-matrix to build a part of concept lattice, in which the intension of every concept be put into one-to-one correspondence with a closed frequent itemset. Then all association rules are discovered by 4 operators which are defined in this paper performed on these concepts.