DocumentCode :
2544832
Title :
Mining frequent maximal cliques efficiently by global view graph
Author :
Lee, Guanling ; Peng, Sheng-Lung ; Kuo, Shih-Wei ; Chen, Yi-Chun
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Dong Hwa Univ., Hualien, Taiwan
fYear :
2012
fDate :
29-31 May 2012
Firstpage :
1362
Lastpage :
1366
Abstract :
Graph mining problem has been a popular research issue in recent years. Many kind of data can be represented as a graph and solve the particular problem by using a specific graph algorithm. Recently, the applications of graph mining are growing quantity. In this paper, the main subject is to find a specific topology called clique which is maximal and frequent in a set of graphs. In our approach, the graphs are first summarized into a global view graph. It is shown that any clique contains in the graph database, there must exist an isomorphic subgraph in the summarized graph according to our summarization process. Therefore, the frequent maximal clique mining process will focus on the global view graph. Moreover, by comparing with other existing methods, a set of experiments is performed to show the benefit of our approach.
Keywords :
data mining; data structures; graph theory; data representation; frequent maximal cliques mining; global view graph; graph algorithm; graph database; graph mining; isomorphic subgraph; specific topology; summarization process; Algorithm design and analysis; Data mining; Itemsets; Knowledge based systems; Testing; Topology; Frequent subgraph mining; Graph mining; Graph summary; Maximal clique mining;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2012 9th International Conference on
Conference_Location :
Sichuan
Print_ISBN :
978-1-4673-0025-4
Type :
conf
DOI :
10.1109/FSKD.2012.6233927
Filename :
6233927
Link To Document :
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