DocumentCode :
655141
Title :
An Efficient Method for Computing Similarity Between Frequent Subgraphs
Author :
Kisung Park ; Yongkoo Han ; Young-Koo Lee
Author_Institution :
Dept. of Comput. Eng., Kyung Hee Univ., Suwon, South Korea
fYear :
2013
fDate :
Sept. 30 2013-Oct. 2 2013
Firstpage :
566
Lastpage :
567
Abstract :
Frequent sub graph mining and graph similarity measures are fundamental and prominent graph analytical techniques. These techniques are often applied together in many graph mining techniques such as clustering and classification. However, these techniques suffer from long running times because frequent sub graph mining and graph similarity measures have been applied independently. In this paper, we propose an efficient method that measures similarity between frequent sub graphs. Our method exploits byproducts of frequent sub graph mining for avoiding costly common sub graph search required in similarity measures. Through experiments on real world graph data, we show that our method measures similarities among all pair of frequent sub graphs within practical time.
Keywords :
data mining; graph theory; classification; clustering; frequent subgraph mining; graph analytical techniques; graph similarity measures; real world graph data; Cloud computing; Computers; Conferences; Current measurement; Data mining; Educational institutions; Time measurement; frequent subgraph; graph similarity measure; maximum common subgraph;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Cloud and Green Computing (CGC), 2013 Third International Conference on
Conference_Location :
Karlsruhe
Type :
conf
DOI :
10.1109/CGC.2013.97
Filename :
6686089
Link To Document :
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