Subgraph isomorphism detection with support for continuous labels

Proceedings of the 23rd International Florida Artificial Intelligence Research Society Conference, FLAIRS-23

Volumn , Issue , 2010, Pages 56-61

  Perez, Gerardo ^a   Olmos, Ivan ^a   Gonzalez, Jesus A ^b  

^a BENEMÉRITA UNIVERSIDAD AUTÓNOMA DE PUEBLA   (Mexico)

^b INSTITUTO NACIONAL DE ASTROFÍSICA   (Mexico)

Author keywords

[No Author keywords available]

Indexed keywords

DISCRETE EVENT SIMULATION; METADATA;

DISCRETIZATIONS; SUBGRAPH ISOMORPHISM; THIRD PHASE; THREE PHASIS; UNSUPERVISED DISCRETIZATION;

SET THEORY;

EID: 77957878515     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (21)

1. Agre, G., and Peev, S. 2002. On supervised and unsupervised discretization. Cybernetics And Information Technologies 2(2): 43-57.
2. Biba, M.; Esposito, F; Ferilli, S.; Mauro, N. D.; and Basile, T. M. A. 2007. Unsupervised discretization using kernel density estimation. Proceedings of the 20th International Joint Conference on Artificial Intelligence 696-701.
3. Birgé, L., and Rozenholc, Y 2002. How many bins should be put in a regular histogram. Technological Report, Laboratoire Probabilits et Modles Alatoires, Universit Pierre et Marie Curie, Paris, France.
4. Borgelt, C., and Berthold, M. R. 2002. Mining molecular fragments: Finding relevant substructures of molecules. In Proceedings of the 2002 International Conference on Data Mining (ICDM02 51-58.
5. Boulle, M. 2005. Optimal bin number for equal frequency discretizations in supervized learning. Intelligent Data Analysis 9(2): 175-188.
6. Cook, D. J., and Holder, L. B. 1994. Substructure discovery using minimum description length and background knowledge. Journal of Artificial Intelligence Research 1: 231-255.
7. Cook, D. J.; Holder, L. B.; and Djoko, S. 1995. Knowledge discovery from structural data. Journal of Intelligence and Information Sciences 5(3): 229-245.
8. Dougherty, J.; Kohavi, R.; and Sahami, M. 1995. Supervised and unsupervised discretization of continuous features. In Proceedings of the Twelfth International Conference on Machine Learning 194-202.
9. Freedman, D., and Diaconis, P. 1981. On the histogram as a density estimator: L2 theory. Zeitschrift for Wahrscheinlichkeitstheorie und verwandte Gebiete 57(4) :453-476.
10. Ghazizadeh, S., and Chawathe, S. S. 2002. Seus: Structure extraction using summaries. In Proc. of the 5th International Conference on Discovery Science.
11. Han, J., and Kamber, M. 2006. Data Mining, Concepts and Techniques. Morgan Kaufmann Publishers, 2nd edition.
12. Inokuchi, A.; Washio, T.; and Motoda, H. 2000. An apriori-based algorithm for mining frequent substructures from graph data. In Proceedings of the 2000 European Symposium on the Principle of Data Mining and Knowledge Discovery (PKDDOO) 13-23.
13. Kuramochi, M., and Karypis, G. 2002. An efficient algorithm for discovering frequent subgraphs. Technical Report, Department of Computing Science, University of Minnesota.
14. Nijssen, S., and Kok, J. N. 2004. A quickstart in frequent structure mining can make a difference. In Proceedings of the 2004 ACM SIGKDD International Conference on Kowledge Discovery and Data Mining (KDD04) 647-652.
15. Olmos, I.; Gonzalez, J. A.; and Osorio, M. 2005. Subgraph isomorphism detection using a code based representation. Proceedings of the 18th International FLAIRS Conference 474-479. (Pubitemid 43251866)
16. Schmidberger, G., and Frank, E. 2005. Unsupervised discretization using tree-based density estimation. In Proceedings 9th European Conference on Principles and Practice of Knowledge Discovery in Databases 240-251.
17. Scoot, D. W. 1979. On optimal and data-based histograms. Biometrika 66(3): 605-610.
18. Sturges, H. A. 1926. The choice of a class interval. J. American Statistical Association 65-66.
19. Wörlein, M.; Dreweke, E.; Meinl, T.; and Fischer, I. 2006. Edgar: the embedding-based graph miner. In Proceedings of the International Workshop on Mining and Learning with Graphs 5: 221-228.
20. Yan, X., and Han., J. 2002. gspan: Graph based substructure pattern mining. Proceedings of the IEEE, International conference on Data Mining 51-58.
21. Yang, Y., and Webb, G. I. 2002. A comparative study of discretization methods for naive-bayes classifiers. 159-173.