DocumentCode :
769239
Title :
Discovering Frequent Graph Patterns Using Disjoint Paths
Author :
Gudes, Ehud ; Shimony, Solomon Eyal ; Vanetik, Natalia
Author_Institution :
Dept. of Comput. Sci., Ben-Gurion Univ. of the Negev, Beer-Sheva
Volume :
18
Issue :
11
fYear :
2006
Firstpage :
1441
Lastpage :
1456
Abstract :
Whereas data mining in structured data focuses on frequent data values, in semistructured and graph data mining, the issue is frequent labels and common specific topologies. The structure of the data is just as important as its content. We study the problem of discovering typical patterns of graph data, a task made difficult because of the complexity of required subtasks, especially subgraph isomorphism. In this paper, we propose a new apriori-based algorithm for mining graph data, where the basic building blocks are relatively large, disjoint paths. The algorithm is proven to be sound and complete. Empirical evidence shows practical advantages of our approach for certain categories of graphs
Keywords :
data mining; graph theory; apriori-based algorithm; disjoint paths; frequent graph pattern discovery; graph data mining; subgraph isomorphism; Computer Society; Data mining; Image databases; Motion pictures; Object oriented databases; Object oriented modeling; Relational databases; Topology; Web mining; XML; Database applications; Web mining; data mining; graph mining.; mining methods and algorithms;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2006.173
Filename :
1704798
Link To Document :
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