Learning closed sets of labeled graphs for chemical applications

Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volumn 3625, Issue , 2005, Pages 190-208

  Kuznetsov, Sergei O ^a   Samokhin, Mikhail V ^a  

^a All Russian Institute for Scientific and Technical Information   (Russian Federation)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; GRAPH THEORY; PARAMETER ESTIMATION; SET THEORY;

COMPUTATION OVERLOADS; SUBGRAPHS;

LEARNING SYSTEMS;

EID: 26944447521     PISSN: 03029743     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1007/11536314_12     Document Type: Conference Paper

References (38)

1. King, R., Srinivasan, A., Dehaspe, L.: WARMR: A Data Mining tool for chemical data. J. of Computer-Aided Molecular Design 15 (2001) 173-181
2. Kramer, S.: Structural Regression Trees. In: Proc. 13th National Conference on Artificial Intelligence, AAAI-96, Cambridge/Menlo Park, AAAI Press/MIT Press (1996) 812-819
3. Kuznetsov, S.: Learning of Simple Conceptual Graphs from Positive and Negative Examples. In Zytkow, J., Rauch, J., eds.: Proc. Principles of Data Mining and Knowledge Discovery, Third European Conference, PKDD'99. Volume 1704 of Lecture Notes in Artificial Intelligence., Springer (1999) 384-392
4. Borgelt, C., Berthold, M.: Mining Molecular Fragments: Finding Relevant Substructures of Molecules. In Zhong, N., Yu, P., eds.: Proc. 2nd IEEE International Conference on Data Mining, ICDM'02, Piscataway, NJ, USA, IEEE Press (2002) 51-58
5. Inokuchi, A., Washio, T., Motoda, H.: Complete Mining of Frequent Patterns from Graphs: Mining Graph Data. Machine Learning 50 (2003) 321-354
6. Washio, T., Motoda, H.: State of the art of graph-based data mining. SIGKDD Explorations Newsletter 5 (2003) 59-68
7. Yan, X., Han, J.: gSpan: Graph-Based Substructure Pattern Mining. In: Proc. IEEE Int. Conf. on Data Mining, ICDM'02, IEEE Computer Society (2002) 721-724
8. Yan, X., Han, J.: CloseGraph: mining closed frequent graph patterns. In Getoor, L., Senator, T., Domingos, P., Faloutsos, C., eds.: Proc. of the 9th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, KDD'03, ACM Press (2003) 286-295
9. Gonzalez, J., Holder, L., Cook, D.: Experimental Comparison of Graph-Based Relational Concept Learning with Inductive Logic Programming System. In Matwin, S., Sammut, C., eds.: Proc. Inductive Logic Programming, ILP'2002. Volume 2583 of Lecture Notes in Artificial Intelligence., Springer (2003) 84-100
10. Blinova, V., Dobrynin, D., Finn, V., Kuznetsov, S., Pankratova, E.: Toxicology analysis by means of the JSM-method. Bioinformatics 19 (2003) 1201-1207
11. Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.; Efficient Mining of Association Rules Using Closed Itemset Lattices. J. Inf. Systems 24 (1999) 25-46
12. Avidon, V., Pomerantsev, A.: Structure-Activity Relationship Oriented Languages for Chemical Structure Representation. J. Chem. Inf. Comput. Sci. 22 (1982) 207-214
13. Helma, C., King, R., Kramer, S., Srinvasan, A., eds.: Proc. of the Workshop on Predictive Toxicology Challegnge at the 5th Conference on Data Mining and Knowledge Discovery, PKDD'01, http://www.predictive-toxicology.org/ ptc/ (2001, September 7)
14. Pfahringer, B.: (The Futility of) Trying to Predict Carcinogenicity of Chemical Compounds. In Helma, C., King, R., Kramer, S., Srinvasan, A., eds.: Proc. of the Workshop on Predictive Toxicology Challegnge at the 5th Conference on Data Mining and Knowledge Discovery, PKDD'01, http://www.predictive- toxicology.org/ptc/ (2001, September 7)
15. Kuznetsov, S.: Similarity operation on hypergraphs as a basis of plausible inference. In: Proc. 1st Soviet Conference on Artificial Intelligence. (1988) 442-448
16. Kuznetsov, S.: JSM-method as a machine learning method. Itogi Nauki i Tekhniki, ser. Informatika 15 (1991) 17-50 in Russian.
17. Kuznetsov, S., Obiedkov, S.: Comparing performance of algorithms for generating concept lattices. J. Exp. Theor. Artif. Intell. 14 (2002) 189-216
18. Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Berlin, Heidelberg (1999)
19. Ganter, B., Kuznetsov, S.: Pattern Structures and Their Projections. In Stumme, G., Delugach, H., eds.: Proc. 9th Int. Conf. on Conceptual Structures, ICCS'01. Volume 2120 of Lecture Notes in Artificial Intelligence., Springer (2001) 129-142
20. Ganter, B., Grigoriev, P., Kuznetsov, S., Samokhin, M.: Concept-Based Data Mining with Scaled Labeled Graphs. In Delugach, H., Pfeiffer, H., Wolff, K., eds.: Proc. 12th Int. Conf. on Conceptual Structures, ICCS'04. Volume 3127 of Lecture Notes in Artificial Intelligence., Springer (2004) 94-108
21. Mitchell, T.: Machine Learning. The McGraw-Hill Companies (1997)
22. Finn, V.: Plausible Reasoning in Systems of JSM Type. Itogi Nauki i Tekhniki, Seriya Informatika 15 (1991) 54-101 in Russian.
23. Yan, L.S.: Study of carcinogenic mechanism of polycyclic aromatic hydrocarbons-extended bay region theory and its quantitative model. Carcinogenesis 6 (1985) 1-6
24. Birkhoff, G.: Lattice Theory. Amer. Math. Soc., Providence (1979)
25. Grigoriev, P.A., Yevtushenko, S.A.: Elements of an Agile Discovery Environment. In Grieser, G., Tanaka, Y., Yamamoto, A., eds.: Proc. 6th International Conference on Discovery Science, ICDS'03. Volume 2843 of Lecture Notes in Artificial Intelligence., Springer (2003) 309-316
26. Ganter, B., Kuznetsov, S.: Formalizing Hypotheses with Concepts. In Ganter, B., Mineau, G., eds.: Proc. 8th Int. Conf. on Conceptual Structures, ICCS'00. Volume 1867 of Lecture Notes in Artificial Intelligence., Springer (2000) 342-356
27. Witten, I., E.Frank: Data Mining: Practical Machine Learning Tools with Java Implementations. Morgan kaufmann, San Francisco (2000)
28. Cook, D., Holder, L.: Graph-Based Data Mining. IEEE Intelligent Systems 15 (2000) 32-41
29. Cameron-Jones, R., Quinlan, J.: Efficient Top-down Induction of Logic Programs. SIGART Bulletin 5 (1994) 33-42
30. Muggleton, S.: Inverse Entailment and Progol. New Generation Computing 13 (1995) 245-286
31. Gonzalez, J., Holder, L., Cook, D.: Application of Graph-Based Concept Learning to the Predictive Toxicology Domain. In Helma, C., King, R., Kramer, S., Srinvasan, A., eds.: Proc. Workshop on Predictive Toxicology Challegnge at the 5th Conference on Data Mining and Knowledge Discovery, PKDD'01, http://www.predictive-toxicology.org/ptc/ (2001, September 6)
32. Blinova, V.G., Dobrynin, D.A., Zholdakova, Z.I., Kharchevnikova, N.V.: Studies on the structure-activity relationships of alcohols by means of the JSM-method. Nauch. Tekh. Inf., ser. 2 (2001) 13-18 in Russian.
33. Guilian, W., Naibin, B.: Structure-activity relationships for rat and mouse LD50 of miscellaneous alcohols. Chemosphere 35 (1998) 1475-1483
34. Woo, Y.T., Lai, D., McLain, J., et al.: Use of mechanism-based structure-activity relationships analysis in carcinogenic ranking for drinking water desinfection by-products. Environ. Health Perspect. (2002) 75-87
35. Kharchevnikova, N.V., Blinova, V.G., Dobrynin, D.A., Maksin, M.V., Zholdakova, Z.I.: Application of JSM-method and quantum-chemical computations for predicting of carcinogenic potential and chronic toxicity in halogen-substituted aliphatic hydrocarbons. Nauch. Tekh. Inf., ser. 2 (2004) 21-28 in Russian.
36. Jerina, D., Lehr, R.: The bay-region theory: quantum mechanical approach to aromatic hydrocarbon-induced carcinogenecity. In: Microsomes and Drug Oxidation. Pergamon Press, Oxford (1977) 709-720
37. Dipple, A.: Polynuclear Aromatic Carcinogens. Number 172 in ACS Monograph. In: Chemical Carcinogens. Amer. Chem. Soc., Washington, DC (1976) 245-314
38. Lowe, J., Silverman, B.: Mo theory of ease of formation of carbocations derived from nonalternant polycyclic aromatic hydrocarbons. J. Amer. Chem. Soc. 106 (1984) 5955-5958