Abstract
The Apriori-based graph mining method is an extension of the Apriori algorithm for association rule mining. It constructs a lattice of graph nodes, in which a node at the k-th level of the lattice has k vertices and the number of supporting instances exceeds a user-specified minimum support. The method can devise a rule "IF subgraph Ga is in transaction G, the union of subgraphs GaUGb is also contained in G with a ceratin confidence level". When we give a transaction consisting of a chemical graph and virtual vertices expressing molecular properties, we can obtain rules representing structure activity relationships. The method was used to analyze mutagenicity data for 230 aromatic nitro compounds. Several interesting substructures were found to affect the mutagenicity.
