An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
Akihiro Inokuchi4, Takashi Washio4 
and Hiroshi Motoda4
| (4) |
I.S.I.R., Osaka University, 8-1, Mihogaoka, 567-0047 Osaka, Ibarakishi, Japan |
Abstract
This paper proposes a novel approach named AGM to efficiently mine the association rules among the frequently appearing sub-structures
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
artificial simulation data and the carcinogenesis data of Oxford University and NTP. Its high efficiency has been confirmed
for the size of a real-world problem....
Currently beeing in Tokyo Research Institute, IBM, 1623-14 Shimotsuruma, Yamatoshi, Kanagawa, 242-8502, Japan.
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