Clustering graphs by weighted substructure mining

ACM International Conference Proceeding Series

Volumn 148, Issue , 2006, Pages 953-960

  Tsuda, Koji ^a   Kudo, Taku ^b  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^b Google Inc   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BIOINFORMATICS; DATA PROCESSING; GRAPH THEORY; PARAMETER ESTIMATION; RNA; STATISTICAL METHODS; TEXT PROCESSING;

BINARY FEATURE; EM ALGORITHM; GRAPH KERNELS; SPECTRAL GRAPH; SUBSTRUCTURE MINING;

DATA MINING;

EID: 34250781669     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1143844.1143964     Document Type: Conference Paper

References (16)

1. Gan, H., Pasquali, S., & Schlick, T. (2003). Exploring the repertoire of RNA secondary motifs using graph theory: Implications for RNA design. Nucleic Acids Res., 31, 2926-2943.
2. Gärtner, T., Flach, P., & Wrobel, S. (2003). On graph kernels: Hardness results and efficient alternatives. Proceedings of 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop (COLT) (pp. 129-143). Springer Verlag.
3. Gasteiger, J., & Engel, T. (2003). Chemoinformatics: a textbook. Wiley-VCH.
4. Griffiths-Jones, S., Moxon, S., Marshall, M., Khanna, A., Eddy, S. R., & Bateman, A. (2005). Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res, 33, 121-124.
5. Hofacker, I. L., Fontana, W., Stadler, P. F., Bonhoeffer, L. S., Tacker, M., & Schuster, P. (1994). Fast Folding and Comparison of RNA Secondary structures. Monatsh. Chemie, 125, 167-188.
6. Inokuchi, A. (2004). Mining generalized substructures from a set of labeled graphs. Proceedings of the 4th IEEE International Conference on Data Mining (ICDM) (pp. 415-418). IEEE Computer Society.
7. Inokuchi, A., Washio, T., & Motoda, H. (2000). An apriori algorithm for mining frequent substructures from graph data. European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) (pp. 13-23).
8. Karklin, Y., Meraz, R., & Holbrook, S. (2005). Classification of non-coding RNA using graph representation of secondary structure. Pac. Symp. Biocomput., 4-15.
9. Kashima, H., Tsuda, K., & Inokuchi, A. (2003). Marginalized kernels between labeled graphs. Proceedings of the 20th International Conference on Machine Learning (pp. 321-328). Menlo Park, CA, AAAI Press.
10. Kudo, T., Maeda, E., & Matsumoto, Y. (2005). An application of boosting to graph classification. In L. Saul, Y. Weiss and L. Bottou (Eds.), Advances in neural information processing systems 17, 729-736. Cambridge, MA: MIT Press.
11. McLachlan, G., & Basford, K. (1998). Mixture models: Inference and applications to clustering. Marcel Dekker.
12. Roth, V., & Lange, T. (2004). Feature selection in clustering problems. In S. Thrun, L. Saul and B. Schölkopf (Eds.), Advances in neural information processing systems 16. Cambridge, MA: MIT Press.
13. Sanfeliu, A., & Fu, K. (1983). A distance measure between attributed relational graphs for pattern recognition. IEEE Trans. Syst. Man Cybern., 13, 353-362.
14. Wilson, R., Hancock, E., & Luo, B. (2005). Pattern vectors from algebraic graph theory. IEEE Trans. Patt. Anal. Mach. Intell., 27, 1112-1124.
15. Yan, X., & Han, J. (2002). gspan: Graph-based substructure pattern mining. Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM) (pp. 721-724). IEEE Computer Society.
16. Yan, X., & Han, J. (2003). CloseGraph: mining closed frequent graph patterns. Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (pp. 286-295). ACM.