TY - GEN
T1 - Frequent subgraph discovery
AU - Kuramochi, Michihiro
AU - Karypis, George
PY - 2001
Y1 - 2001
N2 - As data mining techniques are being increasingly applied to non-traditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the requirement of these domains. An alternate way of modeling the objects in these data sets is to use graphs. Within that model, the problem of finding frequent patterns becomes that of discovering subgraphs that occur frequently over the entire set of graphs. In this paper we present a computationally efficient algorithm for finding all frequent subgraphs in large graph databases. We evaluated the performance of the algorithm by experiments with synthetic datasets as well as a chemical compound dataset. The empirical results show that our algorithm scales linearly with the number of input transactions and it is able to discover frequent subgraphs from a set of graph transactions reasonably fast, even though we have to deal with computationally hard problems such as canonical labeling of graphs and subgraph isomorphism which are not necessary for traditional frequent itemset discovery.
AB - As data mining techniques are being increasingly applied to non-traditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the requirement of these domains. An alternate way of modeling the objects in these data sets is to use graphs. Within that model, the problem of finding frequent patterns becomes that of discovering subgraphs that occur frequently over the entire set of graphs. In this paper we present a computationally efficient algorithm for finding all frequent subgraphs in large graph databases. We evaluated the performance of the algorithm by experiments with synthetic datasets as well as a chemical compound dataset. The empirical results show that our algorithm scales linearly with the number of input transactions and it is able to discover frequent subgraphs from a set of graph transactions reasonably fast, even though we have to deal with computationally hard problems such as canonical labeling of graphs and subgraph isomorphism which are not necessary for traditional frequent itemset discovery.
UR - http://www.scopus.com/inward/record.url?scp=78149312583&partnerID=8YFLogxK
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M3 - Conference contribution
AN - SCOPUS:78149312583
SN - 0769511198
SN - 9780769511191
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 313
EP - 320
BT - Proceedings - 2001 IEEE International Conference on Data Mining, ICDM'01
T2 - 1st IEEE International Conference on Data Mining, ICDM'01
Y2 - 29 November 2001 through 2 December 2001
ER -