DB-FSG: An SQL-Based approach for frequent subgraph mining

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5181 LNCS, Issue , 2008, Pages 684-692

  Chakravarthy, Sharma ^a   Pradhan, Subhesh ^a  

^a The University of Texas at Arlington   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BI-DIRECTIONAL; COMPUTER NETWORKS; DUPLICATE ELIMINATION; EXPERIMENTAL EVALUATIONS; FREQUENT SUBGRAPH MINING; FREQUENT SUBGRAPHS; GRAPH MINING; GRAPH REPRESENTATIONS; INTERNATIONAL CONFERENCES; MAIN MEMORIES; ONE-FORM; RELATIONAL DATABASES; RELATIONAL OPERATORS; SOCIAL NETWORKS;

ADMINISTRATIVE DATA PROCESSING; ARTIFICIAL INTELLIGENCE; CHLORINE COMPOUNDS; DECISION SUPPORT SYSTEMS; EXPERT SYSTEMS; GRAPH THEORY; INTERNET; LABELING; MANAGEMENT INFORMATION SYSTEMS; MINING; SET THEORY; WORLD WIDE WEB;

DATABASE SYSTEMS;

EID: 52949121451     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-85654-2_59     Document Type: Conference Paper

References (11)

1. Chakravarthy, S., Beera, R., Balachandran, R.: Database approach to graph mining. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 341-350. Springer, Heidelberg (2004)
2. Cook, D.J., Holder, L.B.: Graph-based data mining. IEEE Intelligent Systems 15(2), 32-41 (2000)
3. Inokuchi, A., Washio, T., Motoda, H.: Complete mining of frequent patterns from graphs: Mining graph data. Mach. Learn. 50(3) (2003)
4. Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: ICDM 2001: Proc. of the 2001 IEEE International Conference on Data Mining, Washington, DC, USA, pp. 313-320. IEEE Computer Society, Los Alarnitos (2001)
5. Mishra, P., Chakravarthy, S.: Performance evaluation and analysis of k-way join variants for association rule mining. In: James, A., Younas, M., Lings, B. (eds.) BNCOD 2003. LNCS, vol. 2712, pp. 95-114. Springer, Heidelberg (2003)
6. Padmanabhan, S.: HDB-Subdue, a relational database approach to graph mining and hierarchial reduction. Master's thesis, CSE Dept., U T Arlington (2004)
7. Pradhan, S.: A Relational Database Approach to Frequent Subgraph (FSG) Mining. Master's thesis, The University of Texas at Arlington (August 2006), http://itiab.uta.edu/ITLABWEB/Students/sharma/theses/Pra06MS.pdf
8. Rissanen, J.: Stochastic Complexity in Statistical Inquiry Theory. World Scientific Publishing Co., Singapore (1989)
9. Sarawagi, S., Thomas, S., Agrawal, R.: Integrating mining with relational database systems: Alternatives and implications. In: SIGMOD Conference, pp. 343-354 (1998)
10. Washio, T., Motoda, H.: State of the art of graph-based data, mining. SIGKDD Explor. Newsl. 5(1), 59-68 (2003)
11. Yan, X., Han, J.: gSpan: Graph-based substructure pattern mining. In: ICDM 2002: Proc. of the 2002 IEEE Int. Conf. on Data Mining, pp. 721-731 (2002)