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Published Articles >> Table of Contents >> Abstract
22nd International Conference on Data Engineering (ICDE'06)
p. 73
CLAN: An Algorithm for Mining Closed Cliques from Large Dense Graph Databases
Jianyong Wang, Tsinghua University, China
Zhiping Zeng, Tsinghua University, China
Lizhu Zhou, Tsinghua University, China
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2006.34
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| Abstract |
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Most previously proposed frequent graph mining algorithms
are intended to find the complete set of all frequent,
closed subgraphs. However, in many cases only
a subset of the frequent subgraphs with a certain topology
is of special interest. Thus, the method of mining
the complete set of all frequent subgraphs is not suitable
for mining these frequent subgraphs of special interest
as it wastes considerable computing power and space
on uninteresting subgraphs. In this paper we develop
a new algorithm, CLAN, to mine the frequent closed
cliques, the most coherent structures in the graph setting.
By exploring some properties of the clique pattern,
we can simplify the canonical label design and the
corresponding clique (or subclique) isomorphism testing.
Several effective pruning methods are proposed to
prune the search space, while the clique closure checking
scheme is used to remove the non-closed clique patterns.
Our empirical results show that CLAN is very
efficient for large dense graph databases with which the
traditional graph mining algorithms fail. The novelty
of our method is further demonstrated by the application
of CLAN in mining highly correlated stocks from
large stock market data.
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 Additional Information
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Citation:
Jianyong Wang, Zhiping Zeng, Lizhu Zhou,
"CLAN: An Algorithm for Mining Closed Cliques from Large Dense Graph Databases,"
icde,
p. 73,
22nd International Conference on Data Engineering (ICDE'06),
2006
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