LCGMiner: levelwise closed graph pattern mining from large databases

Author

Xu, Aihua ; Lei, Hansheng

Author_Institution

Dept. of Comput. Sci. & Eng., State Univ. of New York, USA

fYear

2004

fDate

21-23 June 2004

Firstpage

421

Lastpage

422

Abstract

LCGMiner (levelwise closed graph pattern miner) is proposed to improve CloseGraph (Yan and Han, 2003) in discovering frequent closed sub graphs. Frequent closed edgesets with the same extended vertexsets are expanded in pattern generation compared to one edge or one vertex in traditional methods. Experiments on synthetic datasets as well as a real NIH dataset demonstrates that our algorithm outperforms CloseGraph and gSpan.

Keywords

data mining; graph theory; pattern recognition; very large databases; CloseGraph; LCGMiner; NIH dataset; closed edgesets; extended vertexsets; frequent closed subgraph discovery; gSpan; large databases; levelwise closed graph pattern mining; pattern generation; Computer science; Data mining; Databases; Itemsets;

fLanguage

English

Publisher

ieee

Conference_Titel

Scientific and Statistical Database Management, 2004. Proceedings. 16th International Conference on

ISSN

1099-3371

Print_ISBN

0-7695-2146-0

Type

conf

DOI

10.1109/SSDM.2004.1311240

Filename

1311240

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3134670