Online structural graph clustering using frequent subgraph mining

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

Volumn 6323 LNAI, Issue PART 3, 2010, Pages 213-228

  Seeland, Madeleine ^a   Girschick, Tobias ^a   Buchwald, Fabian ^a   Kramer, Stefan ^a  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTERING APPROACH; COMMON SUBGRAPH; FREQUENT SUBGRAPH MINING; FREQUENT SUBGRAPHS; GRAPH CLUSTERING; GRAPH DATABASE; MOLECULAR GRAPHS; NOVEL METHODS; ONLINE MODES; PRACTICAL PROBLEMS; PROBLEM FORMULATION; REAL WORLD DATA; STRUCTURAL CLUSTERING; STRUCTURAL GRAPH; SUBGRAPHS; VERTEX CONNECTIVITY;

DATABASE SYSTEMS; LEARNING SYSTEMS;

CLUSTERING ALGORITHMS;

EID: 77958054819     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15939-8_14     Document Type: Conference Paper

References (18)

1. Inokuchi, A., Washio, T., Motoda, H.: An APriori-based algorithm for mining frequent substructures from graph data. In: PKDD '00: Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, pp. 13-23 (2000)
2. Yan, X., Han, J.: gSpan: Graph-based substructure pattern mining. In: Proceedings of the 2002 IEEE International Conference on Data Mining, pp. 721-724 (2002)
3. Tsuda, K., Kudo, T.: Clustering Graphs by Weighted Substructure Mining. In: Cohen, W.W., Moore, A. (eds.) ICML 2006, pp. 953-960. ACM Press, New York (2006)
4. Stahl, M., Mauser, H.: Database clustering with a combination of fingerprint and maximum common substructure methods. J. Chem. Inf. Model. 45, 542-548 (2005)
5. Tsuda, K., Kurihara, K.: Graph mining with variational Dirichlet process mixture models. In: Proceedings of the 8th SIAM International Conference on Data Mining, pp. 432-442 (2008)
6. Martin, Y.C., Kofron, J.L., Traphagen, L.M.: Do structurally similar molecules have similar biological activity? J. Med. Chem. 45, 4350-4358 (2002)
7. Weinstein, J., Kohn, K., Grever, M., Viswanadhan, V.: Neural computing in cancer drug development: Predicting mechanism of action. Science 258, 447-451 (1992)
8. Koutsoukos, A.D., Rubinstein, L.V., Faraggi, D., Simon, R.M., Kalyandrug, S., Weinstein, J.N., Kohn, K.W., Paull, K.D.: Discrimination techniques applied to the NCI in vitro anti-tumour drug screen: predicting biochemical mechanism of action. Stat. Med. 13, 719-730 (1994)
9. Raymond, J.W., Willett, P.: Effectiveness of graph-based and fingerprint-based similarity measures for virtual screening of 2D chemical structure databases. J. Comput. Aided. Mol. Des. 16(1), 59-71 (2002)
10. McGregor, M.J., Pallai, P.V.: Clustering of large databases of compounds: Using the MDL "keys" as structural descriptors. J. Chem. Inform. Comput. Sci. 37(3), 443-448 (1997)
11. Yoshida, T., Shoda, R., Motoda, H.: Graph clustering based on structural similarity offragments. In: Jantke, K.P., et al. (eds.) Federation over the Web. LNCS (LNAI), vol. 3847, pp. 97-114. Springer, Heidelberg (2006)
12. Günter, S., Bunke, H.: Validation indices for graph clustering. Pattern Recogn. Lett. 24(8), 1107-1113 (2003)
13. Kohonen, T.: Self-organizing maps. Springer, Heidelberg (1997)
14. Chen, Y.L., Hu, H.L.: An overlapping cluster algorithm to provide non-exhaustive clustering. Eur. J. Oper. Res. 173(3), 762-780 (2006)
15. Raymond, J.W., Blankley, C.J., Willett, P.: Comparison of chemical clustering methods using graph-and fingerprint-based similarity measures. J. Mol. Graph. Model. 21(5), 421-433 (2003)
16. Bunke, H., Foggia, P., Guidobaldi, C., Sansone, C., Vento, M.: A comparison of algorithms for maximum common subgraph on randomly connected graphs. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 123-132. Springer, Heidelberg (2002)
17. Aggarwal, C.C., Ta, N., Wang, J., Feng, J., Zaki, M.: XProj: a framework for projected structural clustering of XML documents. In: KDD '07: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 46-55. ACM, New York (2007)
18. Wang, K., Han, J.: Bide: Efficient mining of frequent closed sequences. In: Inter-national Conference on Data Engineering (2004)