CloseGraph
Top Cited Papers
- 24 August 2003
- proceedings article
- Published by Association for Computing Machinery (ACM)
- p. 286-295
- https://doi.org/10.1145/956750.956784
Abstract
Recent research on pattern discovery has progressed form mining frequent itemsets and sequences to mining structured patterns including trees, lattices, and graphs. As a general data structure, graph can model complicated relations among data with wide applications in bioinformatics, Web exploration, and etc. However, mining large graph patterns in challenging due to the presence of an exponential number of frequent subgraphs. Instead of mining all the subgraphs, we propose to mine closed frequent graph patterns. A graph g is closed in a database if there exists no proper supergraph of g that has the same support as g. A closed graph pattern mining algorithm, CloseGraph, is developed by exploring several interesting pruning methods. Our performance study shows that CloseGraph not only dramatically reduces unnecessary subgraphs to be generated but also substantially increases the efficiency of mining, especially in the presence of large graph patterns.Keywords
This publication has 2 references indexed in Scilit:
- Efficiently mining frequent trees in a forestPublished by Association for Computing Machinery (ACM) ,2002
- Mining frequent patterns without candidate generationPublished by Association for Computing Machinery (ACM) ,2000