Discovery of the D-basis in binary tables based on hypergraph dualization

Discovery of (strong) association rules, or implications, is an important task in data management, and it nds application in arti cial intelligence, data mining and the semantic web. We introduce a novel approach for the discovery of a speci c set of implications, called the D-basis, that provides a representation for a reduced binary table, based on the structure of its Galois lattice. At the core of the method are the D-relation de ned in the lattice theory framework, and the hypergraph dualization algorithm that allows us to e ectively produce the set of transversals for a given Sperner hypergraph. The latter algorithm, rst developed by specialists from Rutgers Center for Operations Research, has already found numerous applications in solving optimization problems in data base theory, arti cial intelligence and game theory. One application of the method is for analysis of gene expression data related to a particular phenotypic variable, and some initial testing is done for the data provided by the University of Hawaii Cancer Cente

Discovery of the D-basis in binary tables based on hypergraph dualization

Discovery of (strong) association rules, or implications, is an important task in data management, and it nds application in arti cial intelligence, data mining and the semantic web. We introduce a novel approach for the discovery of a speci c set of implications, called the D-basis, that provides a representation for a reduced binary table, based on the structure of its Galois lattice. At the core of the method are the D-relation de ned in the lattice theory framework, and the hypergraph dualization algorithm that allows us to e ectively produce the set of transversals for a given Sperner hypergraph. The latter algorithm, rst developed by specialists from Rutgers Center for Operations Research, has already found numerous applications in solving optimization problems in data base theory, arti cial intelligence and game theory. One application of the method is for analysis of gene expression data related to a particular phenotypic variable, and some initial testing is done for the data provided by the University of Hawaii Cancer Cente