Abstract. This paper proposes a novel approach named AGM to eciently mine the association rules among the frequently appearing substructures in a given graph data set. A graph transaction is represented by an adjacency matrix, and the frequent patterns appearing in the matrices are mined through the extended algorithm of the basket analysis. Its performance has been evaluated for the articial simulation data and the carcinogenesis data of Oxford University and NTP. Its high eciency has been conrmed for the size of a real-world problem. . . .
A different approach that attempts to capture structural correlations is mining the frequent substructures in graph data REF .
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD
Computer Science