Discovering Emerging Graph Patterns from Chemicals

1. Borgelt, C., Berthold, M.R.: Mining molecular fragments: Finding relevant substructures of molecules. In: Proceedings of the IEEE International Conference on Data Mining (ICDM 2002), pp. 51–58 (2002)

   Google Scholar 

2. Borgelt, C., Meinl, T., Berthold, M.: Moss: a program for molecular substructure mining. In: Workshop Open Source Data Mining Software, pp. 6–15. ACM Press, New York (2005)

   Google Scholar 

3. Cook, D.J., Holder, L.B.: Mining Graph Data. John Wiley & Sons, Chichester (2006)

   Book  MATH  Google Scholar 

4. De Raedt, L., Kramer, S.: The levelwise version space algorithm and its application to molecular fragment finding. In: IJCAI 2001, pp. 853–862 (2001)

   Google Scholar 

5. Dong, G., Li, J.: Efficient mining of emerging patterns: discovering trends and differences. In: Proceedings of the Fifth International Conference on Knowledge Discovery and Data Mining (ACM SIGKDD 1999), pp. 43–52. ACM Press, New York (1999)

   Chapter  Google Scholar 

6. EPAFHM. Mid continent ecology division (environement protection agency), fathead minnow, http://www.epa.gov/med/Prods_Pubs/fathead_minnow.htm

7. Garey, M.R., Johnson, D.S.: Computers and Intractability. Freeman and Company, New York (1979)

   MATH  Google Scholar 

8. Kramer, S., Raedt, L.D., Helma, C.: Molecular feature mining in HIV data. In: KDD, pp. 136–143 (2001)

   Google Scholar 

9. Li, J., Dong, G., Ramamohanarao, K.: Making use of the most expressive jumping emerging patterns for classification. Knowledge and Information Systems 3(2), 131–145 (2001)

   Article  MATH  Google Scholar 

10. Li, J., Wong, L.: Emerging patterns and gene expression data. Genome Informatics 12, 3–13 (2001)

    Google Scholar 

11. Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery 1(3), 241–258 (1997)

    Article  Google Scholar 

12. Ng, R.T., Lakshmanan, V.S., Han, J., Pang, A.: Exploratory mining and pruning optimizations of constrained associations rules. In: Proceedings of ACM SIGMOD 1998, pp. 13–24. ACM Press, New York (1998)

    Chapter  Google Scholar 

13. Soulet, A., Crémilleux, B.: Mining constraint-based patterns using automatic relaxation. Intelligent Data Analysis 13(1), 1–25 (2009)

    Google Scholar 

14. Soulet, A., Kléma, J., Crémilleux, B.: Efficient Mining under Rich Constraints Derived from Various Datasets. In: Džeroski, S., Struyf, J. (eds.) KDID 2006. LNCS, vol. 4747, pp. 223–239. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

15. Ting, R.M.H., Bailey, J.: Mining minimal contrast subgraph patterns. In: Ghosh, J., Lambert, D., Skillicorn, D.B., Srivastava, J. (eds.) SDM, pp. 638–642. SIAM, Philadelphia (2006)

    Google Scholar 

16. Ullman, J.: An algorithm for subgraph isomorphism. Journal of the ACM 23, 31–42 (1976)

    Article  MathSciNet  MATH  Google Scholar 

17. Veith, G., Greenwood, B., Hunter, R., Niemi, G., Regal, R.: On the intrinsic dimensionality of chemical structure space. Chemosphere 17(8), 1617–1644 (1988)

    Article  Google Scholar 

18. Wörlein, M., Meinl, T., Fischer, I., Philippsen, M.: A quantitative comparison of the subgraph miners mofa, gspan, FFSM, and gaston. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 392–403. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

19. Yan, X., Han, J.: gspan: Graph-based substructure pattern mining. In: ICDM. LNCS, vol. 2394, pp. 721–724. IEEE Computer Society Press, Los Alamitos (2002)

    Google Scholar 

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