R. Agrawal, T. Imielinski, and A. Swami, Database mining: a performance perspective, IEEE Transactions on Knowledge and Data Engineering, vol.5, issue.6, pp.914-925, 1993.
DOI : 10.1109/69.250074

R. Agrawal and R. Srikant, Fast algorithms for mining association rules in large databases, Proceedings of the 20th Conference on Very Large Data Bases (VLDB-94), pp.478-499, 1994.

R. Agrawal and R. Srikant, Mining sequential patterns, Proceedings of the Eleventh International Conference on Data Engineering, pp.3-14, 1995.
DOI : 10.1109/ICDE.1995.380415

R. Agrawal, T. Imielinski, and A. N. Swami, Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, pp.207-216, 1993.

R. Agrawal, H. Mannila, R. Srikant, and H. , Toivonen et A. I. Verkamo : Fast discovery of association rules, Advances in Knowledge Discovery and Data Mining, pp.307-328

T. Anwar, H. Beck, and S. Navathe, Knowledge mining by imprecise querying: a classification-based approach, [1992] Eighth International Conference on Data Engineering, pp.622-630, 1992.
DOI : 10.1109/ICDE.1992.213146

K. Asai, S. Abe, H. Kawasoe, H. Arimura, and . Sakamoto, Arikawa : Efficient substructure discovery from large semi-structured data, 2002.

T. Asai, H. Arimura, T. Uno, and S. Nakano, Discovering Frequent Substructures in Large Unordered Trees, Discovery Science Lecture Notes in Computer Science, vol.2843, pp.47-61, 2003.
DOI : 10.1007/978-3-540-39644-4_6

J. Auer and J. Bajorath, Distinguishing between Bioactive and Modeled Compound Conformations through Mining of Emerging Chemical Patterns, Journal of Chemical Information and Modeling, vol.48, issue.9, pp.1747-1753, 2008.
DOI : 10.1021/ci8001793

F. Baader, D. Calvanese, D. Mcguinness, D. Nardi, and P. Patel-schneider, The Description Logic Handbook, 2002.
DOI : 10.1017/CBO9780511711787

L. Babai, D. Y. Grigoryev, and D. M. Mount, Isomorphism of graphs with bounded eigenvalue multiplicity, Proceedings of the fourteenth annual ACM symposium on Theory of computing , STOC '82, pp.310-324, 1982.
DOI : 10.1145/800070.802206

J. Bailey, T. Manoukian, and K. Ramamohanarao, Fast Algorithms for Mining Emerging Patterns, Lecture Notes in Computer Science, vol.2431, pp.39-50, 2002.
DOI : 10.1007/3-540-45681-3_4

Y. Bastide, N. Pasquier, R. Taouil, G. Stumme, and L. Lakhal, Mining minimal nonredundant association rules using frequent closed itemsets
URL : https://hal.archives-ouvertes.fr/hal-00467751

Y. Bastide, R. Taouil, N. Pasquier, G. Stumme, and L. Lakhal, Mining frequent patterns with counting inference, ACM SIGKDD Explorations Newsletter, vol.2, issue.2, pp.66-75, 2000.
DOI : 10.1145/380995.381017

URL : https://hal.archives-ouvertes.fr/hal-00467750

R. J. Bayardo, Efficiently mining long patterns from databases, pp.85-93, 1998.
DOI : 10.1145/276305.276313

URL : http://www.cs.tau.ac.il/~fiat/dmsem03/Efficiently mining long patterns from databases - 1998.pdf

S. Berasaluce, Fouille de données et acquisition de connaissancesàconnaissancesà partir de bases de données de réactions chimiques, Thèse de chimie informatique et théorique, 2002.

S. Berasaluce, C. Laurenço, A. Napoli, G. Boulicaut, F. Esposito et al., An Experiment on Knowledge Discovery in Chemical Databases, Knowledge Discovery in Databases : PKDD 2004, 8th European Conference on Principles and Practice of Knowledge Discovery in Databases Proceedings, pp.39-51, 2004.
DOI : 10.1007/978-3-540-30116-5_7

URL : https://hal.archives-ouvertes.fr/hal-00162537

C. Berge, The Theory of Graphs and Its Applications, 1962.

S. H. Bertz and T. J. Sommer, Rigorous mathematical approaches to strategic bonds and synthetic analysis based on conceptually simple new complexity indexes, Chem. Commun. (Cambridge), issue.24, pp.242409-2410, 1997.

G. Birkhoff, Lattice theory, 1940.
DOI : 10.1090/coll/025

H. Blockeel, L. D. Raedt, and N. Jacobs, Demoen : Scaling up inductive logic programming by learning from interpretations, Data Mining and Knowledge Discovery, vol.3, issue.1, pp.59-93, 1999.
DOI : 10.1023/A:1009867806624

K. Boda, T. Seidel, and J. Gasteiger, Structure and reaction based evaluation of synthetic accessibility, Journal of Computer-Aided Molecular Design, vol.38, issue.6, pp.311-325, 2007.
DOI : 10.1021/ci9800211

M. Boley, T. Horváth, A. Poigné, and S. Wrobel, Efficient Closed Pattern Mining in Strongly Accessible Set Systems (Extended Abstract), Lecture Notes in Computer Science, vol.4702, pp.382-389, 2007.
DOI : 10.1007/978-3-540-74976-9_37

C. Borgelt and M. R. Berthold, Mining molecular fragments: finding relevant substructures of molecules, 2002 IEEE International Conference on Data Mining, 2002. Proceedings., pp.9-12, 2002.
DOI : 10.1109/ICDM.2002.1183885

J. Boulicaut, A. Bykowski, and C. Rigotti, Free-sets : A condensed representation of boolean data for the approximation of frequency queries, Data Mining and Knowledge Discovery, vol.7, issue.1, pp.5-22, 2003.
DOI : 10.1023/A:1021571501451

URL : https://hal.archives-ouvertes.fr/hal-01503814

L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, 1984.

S. Brin, R. Motwani, J. D. Ullman, and S. Tsur, Dynamic itemset counting and implication rules for market basket data, SIGMOD '97 : Proceedings of the 1997 ACM SIGMOD international conference on Management of data, pp.255-264, 1997.

B. Bringmann and S. Nijssen, What Is Frequent in a Single Graph?, pp.858-863, 2008.
DOI : 10.1007/978-3-540-68125-0_84

B. Bringmann and A. Zimmermann, The Chosen Few: On Identifying Valuable Patterns, Seventh IEEE International Conference on Data Mining (ICDM 2007), pp.63-72, 2007.
DOI : 10.1109/ICDM.2007.85

F. K. Brown, Chemoinformatics: What is it and How does it Impact Drug Discovery., Annual Reports in Med. Chem, vol.33, pp.375-384, 1998.
DOI : 10.1016/S0065-7743(08)61100-8

B. Buchanan and E. Feigenbaum, Dendral and meta-dendral: Their applications dimension, Artificial Intelligence, vol.11, issue.1-2, pp.5-24, 1978.
DOI : 10.1016/0004-3702(78)90010-3

H. Bunke, P. Foggia, C. Guidobaldi, C. Sansone, and M. Vento, A Comparison of Algorithms for Maximum Common Subgraph on Randomly Connected Graphs, Lecture Notes in Computer Science, vol.2396, pp.123-132, 2002.
DOI : 10.1007/3-540-70659-3_12

H. Bunke and K. Shearer, A graph distance metric based on the maximal common subgraph, Pattern Recognition Letters, vol.19, issue.3-4, pp.255-259, 1998.
DOI : 10.1016/S0167-8655(97)00179-7

C. Bibliographie, X. Chen, P. S. Yan, J. Yu, D. Han et al., Towards graph containment search and indexing, pp.926-937, 2007.

H. Cheng, X. Yan, J. Han, and C. Hsu, Discriminative Frequent Pattern Analysis for Effective Classification, 2007 IEEE 23rd International Conference on Data Engineering, pp.716-725, 2007.
DOI : 10.1109/ICDE.2007.367917

Y. Chi, R. R. Muntz, S. Nijssen, and J. N. Kok, Frequent subtree mining -an overview, Fundam. Inform, vol.66, issue.12, pp.161-198, 2005.

Y. Chi, Y. Xia, Y. Yang, and R. R. , Muntz : Mining closed and maximal frequent subtrees from databases of labeled rooted trees, IEEE Trans. Knowl. Data Eng, vol.17, issue.2, pp.190-202, 2005.

Y. Chi, Y. Yang, and R. R. Muntz, Indexing and mining free trees, Third IEEE International Conference on Data Mining, pp.509-512, 2003.
DOI : 10.1109/ICDM.2003.1250964

Y. Chi, Y. Yang, and R. R. Muntz, Hybridtreeminer : An efficient algorithm for mining frequent rooted trees and free trees using canonical form, SSDBM, pp.11-20, 2004.

Y. Chi, Y. Yang, Y. Xia, and R. R. Muntz, CMTreeMiner: Mining Both Closed and Maximal Frequent Subtrees, Lecture Notes in Computer Science, vol.3056, pp.63-73, 2004.
DOI : 10.1007/978-3-540-24775-3_9

A. Colmerauer and P. Roussel, The birth of prolog, HOPL Preprints, pp.37-52, 1993.

D. J. Cook and L. B. Holder, Substructure discovery using minimum description length and background knowledge, J. of Art. Intell. Res, vol.1, pp.231-255, 1994.

D. J. Cook and L. B. , Graph-based data mining, IEEE Intelligent Systems, vol.15, issue.2, pp.32-41, 2000.
DOI : 10.1109/5254.850825

E. Corey, Centenary lecture. Computer-assisted analysis of complex synthetic problems, Quarterly Reviews, Chemical Society, vol.25, issue.4, pp.455-482, 1971.
DOI : 10.1039/qr9712500455

E. Corey and X. Cheng, The Logic of Chemical Synthesis, 1995.

E. Corey and W. Wipke, Computer-Assisted Design of Complex Organic Syntheses, Science, vol.166, issue.3902, pp.178-192, 1969.
DOI : 10.1126/science.166.3902.178

D. G. Corneil and C. C. , An Efficient Algorithm for Graph Isomorphism, Journal of the ACM, vol.17, issue.1, pp.51-64, 1970.
DOI : 10.1145/321556.321562

T. Cover and P. Hart, Nearest neighbor pattern classification Information Theory, IEEE Transactions on, vol.13, issue.1, pp.21-27, 1967.

B. Crémilleux and A. Soulet, Discovering Knowledge from Local Patterns with Global Constraints, Lecture Notes in Computer Science, vol.5073, issue.2, pp.1242-1257, 2008.
DOI : 10.1007/978-3-540-69848-7_99

B. Cuissart, Plus grande structure communè a deux graphes : méthode de calcul et intérêt dans un contexte SAR, Thèse de doctorat, 2004.

L. Dehaspe and L. D. , Mining association rules in multiple relations, Lecture Notes in Computer Science, vol.1297, pp.125-132, 1997.
DOI : 10.1007/3540635149_40

L. Dehaspe, H. Toivonen, R. D. Agrawal, P. Stolorz, and G. Piatetsky-shapiro, King : Finding frequent substructures in chemical compounds, 4th International Conference on Knowledge Discovery and Data Mining, pp.30-36, 1998.

M. Deshpande and M. Kuramochi, Karypis : Frequent sub-structure-based approaches for classifying chemical compounds, ICDM icd, pp.35-42, 2003.

G. Dong and J. Li, Efficient mining of emerging patterns, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '99, pp.43-52, 1999.
DOI : 10.1145/312129.312191

J. Dubois and Y. Sobel, DARC system for documentation and artificial intelligence in chemistry, Journal of Chemical Information and Modeling, vol.25, issue.3, pp.326-333, 1985.
DOI : 10.1021/ci00047a032

H. Ehrig, K. Ehrig, and U. Prange, Taentzer : Fundamentals of Algebraic Graph Transformation . Monographs in Theoretical Computer Science. An EATCS Series, 2006.

T. Fawcett, An introduction to ROC analysis, Pattern Recognition Letters, vol.27, issue.8, pp.861-874, 2006.
DOI : 10.1016/j.patrec.2005.10.010

U. Fayyad, G. Piatetsky-shapiro, and P. Smyth, Knowledge discovery and data mining : Towards a unifying framework, pp.82-88, 1996.

U. Fayyad, G. Piatetsky-shapiro, P. Smyth, and R. Uthurusamy, Advances in Knowledge Discovery and Data Mining, 1996.

U. M. Fayyad, G. Piatetsky-shapiro, and P. Smyth, From data mining to knowledge discovery : An overview, Advances in Knowledge Discovery and Data Mining, pp.1-34, 1996.

U. M. Fayyad, N. Weir, and S. G. Djorgovski, SKICAT: A Machine Learning System for Automated Cataloging of Large Scale Sky Surveys, ICML, pp.112-119, 1993.
DOI : 10.1016/B978-1-55860-307-3.50021-6

S. Bibliographie, O. Ferré, B. Ridoux, and . Sigonneau, Arbitrary relations in formal concept analysis and logical information systems, Lecture Notes in Computer Science, vol.3596, pp.166-180, 2005.

S. Ferré, Systèmes d'information logiques : un paradigme logico-contextuel pour interroger, naviguer et apprendre, Thèse de doctorat, 2002.

M. Fiedler and C. Borgelt, Support computation for mining frequent subgraphs in a single graph, 2007.

I. Fischer and T. Meinl, Graph based molecular data mining - an overview, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), pp.10-13, 2004.
DOI : 10.1109/ICSMC.2004.1401253

H. Fröhlich, J. K. Wegner, and F. Sieker, Zell : Optimal assignment kernels for attributed molecular graphs, Machine Learning, Proceedings of the Twenty-Second International Conference (ICML 2005) de ACM International Conference Proceeding Series, pp.225-232, 2005.

S. Fujita, Description of organic reactions based on imaginary transition structures. 1. Introduction of new concepts, Journal of Chemical Information and Modeling, vol.26, issue.4, pp.205-212, 1986.
DOI : 10.1021/ci00052a009

B. Ganter and R. Wille, Formal Concept Analysis, 1999.

B. Ganter, P. A. Grigoriev, S. O. Kuznetsov, and M. V. , Samokhin : Concept-based data mining with scaled labeled graphs, de Lecture Notes in Computer Science, pp.94-108, 2004.

B. Ganter and S. O. , Pattern Structures and Their Projections, Lecture Notes in Computer Science, vol.2120, pp.129-142, 2001.
DOI : 10.1007/3-540-44583-8_10

URL : http://www.math.tu-dresden.de/~ganter/psfiles/kuznet.ps

B. Ganter and S. O. Kuznetsov, Hypotheses and Version Spaces, Lecture Notes in Computer Science, vol.2746, pp.83-95, 2003.
DOI : 10.1007/978-3-540-45091-7_6

B. Ganter, G. Stumme, and R. Wille, Formal Concept Analysis, Foundations and Applications Lecture Notes in Computer Science, vol.3626, 2005.

M. R. Garey and D. S. Johnson, Computers and Intractability : A Guide to the Theory of NP-Completeness, 1979.

T. Gärtner, A survey of kernels for structured data, ACM SIGKDD Explorations Newsletter, vol.5, issue.1, pp.49-58, 2003.
DOI : 10.1145/959242.959248

J. Gasteiger and W. D. Ihlenfeldt, A collection of computer methods for synthesis design and reaction prediction, Recueil des Travaux Chimiques des Pays-Bas, vol.88, issue.6, pp.270-290, 1992.
DOI : 10.1007/978-3-642-75430-2_30

J. Gasteiger, W. D. Ihlenfeldt, P. Röse, and R. Wanke, Computer-assisted reaction prediction and synthesis design, Analytica Chimica Acta, vol.235, pp.65-75, 1990.
DOI : 10.1016/S0003-2670(00)82062-0

J. Gasteiger and T. Engel, Chemoinformatics : A Textbook, 2004.
DOI : 10.1002/3527601643

J. Gennari, P. Langley, and D. Fisher, Models of incremental concept formation, pp.11-61, 1989.

L. Getoor and C. P. Diehl, Link mining, ACM SIGKDD Explorations Newsletter, vol.7, issue.2, pp.3-12, 2005.
DOI : 10.1145/1117454.1117456

L. Getoor and B. Taskar, Introduction to Statistical Relational Learning, 2007.

O. Gien, Modélisation de la synthèse organique multi-´ etapes. Développement d'outils informatiques d'aidè a la conception de plans de synthèse, Thèse de chimie moléculaire II Sciences et Techniques du Languedoc, 1998.

K. Gouda and M. J. Zaki, GenMax: An Efficient Algorithm for Mining Maximal Frequent Itemsets, Data Mining and Knowledge Discovery, vol.129, issue.2, pp.223-242, 2005.
DOI : 10.1007/s10618-005-0002-x

E. Grosholz and R. Hoffmann, How symbolic and iconic languages bridge the two worlds of the chemist : A case study from contemporary bioorganic chemistry : Of Minds and Molecules : New Philosophical Perspectives on Chemistry, pp.230-257, 2000.

M. R. Hacene, M. Huchard, A. Napoli, and P. Valtchev, A proposal for combining formal concept analysis and description logics for mining relational data, Lecture Notes in Computer Science, vol.4390, pp.51-65, 2007.
URL : https://hal.archives-ouvertes.fr/lirmm-00163364

J. Han, Y. Cai, and N. Cercone, Knowledge discovery in databases : An attribute-oriented approach, Proceedings of the 18th Conference on " Very Large Databases (VLDB'92), pp.547-559, 1992.

J. Han, Data Mining, 2005.
DOI : 10.1145/233269.233324

URL : https://hal.archives-ouvertes.fr/hal-01534761

J. Han, J. Pei, B. Mortazavi-asl, Q. Chen, U. Dayal et al., FreeSpan, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '00, pp.355-359, 2000.
DOI : 10.1145/347090.347167

J. Han and J. Pei, Yin : Mining frequent patterns without candidate generation, pp.1-12

J. Han, J. Pei, Y. Yin, and R. Mao, Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach, Data Mining and Knowledge Discovery, vol.8, issue.1, pp.53-87, 2004.
DOI : 10.1023/B:DAMI.0000005258.31418.83

D. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, Drug Safety, vol.15, issue.2, 2001.
DOI : 10.2165/00002018-200730070-00010

S. Bibliographie, Hanessian : Man, machine and visual imagery in strategic synthesis planning : computerperceived precursors for drug candidates, Curr. Opin. Drug Discovery Dev, vol.8, issue.6, pp.798-819, 2005.

S. Hanessian, J. Franco, and B. Larouche, The psychobiological basis of heuristic synthesis planning - man, machine and the chiron approach, Pure and Applied Chemistry, vol.46, issue.10, pp.1887-1910, 1990.
DOI : 10.1351/pac199062101887

R. Haralick and G. Elliott, Increasing tree search efficiency for constraint satisfaction problems, Artificial Intelligence, vol.14, issue.3, pp.263-313, 1980.
DOI : 10.1016/0004-3702(80)90051-X

M. A. Hasan, V. Chaoji, S. Salem, J. Besson, and M. J. Zaki, ORIGAMI: Mining Representative Orthogonal Graph Patterns, Seventh IEEE International Conference on Data Mining (ICDM 2007), pp.153-162, 2007.
DOI : 10.1109/ICDM.2007.45

URL : https://hal.archives-ouvertes.fr/hal-01512570

C. Helma, T. Cramer, S. Kramer, and L. D. , Data Mining and Machine Learning Techniques for the Identification of Mutagenicity Inducing Substructures and Structure Activity Relationships of Noncongeneric Compounds, Journal of Chemical Information and Computer Sciences, vol.44, issue.4, pp.1402-1411, 2004.
DOI : 10.1021/ci034254q

J. B. Hendrickson, Comprehensive System for Classification and Nomenclature of Organic Reactions, Journal of Chemical Information and Computer Sciences, vol.37, issue.5, pp.852-860, 1997.
DOI : 10.1021/ci970040v

J. B. Hendrickson and A. G. Toczko, SYNGEN program for synthesis design: basic computing techniques, Journal of Chemical Information and Modeling, vol.29, issue.3, pp.137-145, 1989.
DOI : 10.1021/ci00063a001

M. Holsheimer, M. Kersten, and H. Mannila, Toivonen : A perspective on databases and data mining, Proceedings of the First International Conference on Knowledge Discovery & Data Mining (KDD-95), pp.150-155, 1995.

J. E. Hopcroft and J. K. Wong, Linear time algorithm for isomorphism of planar graphs (Preliminary Report), Proceedings of the sixth annual ACM symposium on Theory of computing , STOC '74, pp.172-184, 1974.
DOI : 10.1145/800119.803896

T. Horváth, T. Gärtner, and S. Wrobel, Cyclic pattern kernels for predictive graph mining, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.158-167, 2004.
DOI : 10.1145/1014052.1014072

J. Huan and W. Wang, Prins : Efficient mining of frequent subgraph in the presence of isomorphism, ICDM, pp.549-552, 2003.

J. Huan, W. Wang, J. Prins, and J. Yang, SPIN, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.581-586, 2004.
DOI : 10.1145/1014052.1014123

M. Huchard, M. R. Hacene, C. Roume, and P. Valtchev, Relational concept discovery in structured datasets, Annals of Mathematics and Artificial Intelligence, vol.256, issue.3, pp.1-439, 2007.
DOI : 10.1007/978-94-009-7798-3_15

URL : https://hal.archives-ouvertes.fr/lirmm-00183376

T. Imielinski and H. Mannila, A database perspective on knowledge discovery, Communications of the ACM, vol.39, issue.11, pp.58-64, 1996.
DOI : 10.1145/240455.240472

A. Inokuchi and H. Kashima, Mining significant pairs of patterns from graph structures with class labels, Third IEEE International Conference on Data Mining, pp.83-90, 2003.
DOI : 10.1109/ICDM.2003.1250906

A. Inokuchi, T. Washio, and H. Motoda, An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data, PKDD '00 : Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, pp.13-23, 2000.
DOI : 10.1007/3-540-45372-5_2

P. Jauffret, T. Hander, C. Tonnelier, and G. Kaufmann, Machine learning of generic reactions: 1. Scope of the project; the GRAMS program, Tetrahedron Computer Methodology, vol.3, issue.6, pp.323-333, 1990.
DOI : 10.1016/0898-5529(90)90059-H

P. Jauffret, T. Hanser, J. F. Marchaland, H. Vogel, T. Fang et al., Grams: A network generator for synthetic methods learning in organic chemistry, AIP Conference Proceedings, pp.569-574, 1995.
DOI : 10.1063/1.47761

I. Jonyer, D. J. Cook, and L. B. , Holder : Graph-based hierarchical conceptual clustering, Journal of Machine Learning Research, vol.2, pp.19-43, 2001.

W. L. Jorgensen, E. R. Laird, A. J. Gushurst, J. M. Fleischer, S. A. Gothe et al., CAMEO: a program for the logical prediction of the products of organic reactions, Pure and Applied Chemistry, vol.62, issue.10, pp.621921-1932, 1990.
DOI : 10.1351/pac199062101921

A. Karwath and L. D. , Raedt : Predictive graph mining, Discovery Science Lecture Notes in Computer Science, vol.3245, pp.1-15, 2004.

H. Kashima and Y. Tsuboi, Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs, Twenty-first international conference on Machine learning , ICML '04, 2004.
DOI : 10.1145/1015330.1015383

J. Kazius, S. Nijssen, J. Kok, T. Bäck, and A. P. , Substructure Mining Using Elaborate Chemical Representation, Journal of Chemical Information and Modeling, vol.46, issue.2, pp.597-605, 2006.
DOI : 10.1021/ci0503715

URL : http://www.liacs.nl/~snijssen/publications/substructure.pdf

P. J. Kelly, A congruence theorem for trees, Pacific Journal of Mathematics, vol.7, issue.1, pp.961-968, 1957.
DOI : 10.2140/pjm.1957.7.961

G. Klopman, Artificial intelligence approach to structure-activity studies. Computer automated structure evaluation of biological activity of organic molecules, Journal of the American Chemical Society, vol.106, issue.24, pp.7315-7321, 1984.
DOI : 10.1021/ja00336a004

R. A. Kowalski, Predicate logic as programming language, IFIP Congress, pp.569-574, 1974.

S. Kramer and L. D. , Raedt : Feature construction with version spaces for biochemical applications, pp.258-265, 2001.

S. Kramer, L. D. Raedt, and C. Helma, Molecular feature mining in HIV data, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '01, pp.136-143, 2001.
DOI : 10.1145/502512.502533

M. Kuramochi, Karypis : Frequent subgraph discovery, Proceedings of the 2001 IEEE International Conference on Data Mining, pp.313-320, 2001.
DOI : 10.1109/icdm.2001.989534

URL : http://www-users.itlabs.umn.edu/~karypis/publications/Papers/Postscript/fsg.ps

M. Kuramochi, An efficient algorithm for discovering frequent subgraphs, IEEE Transactions on Knowledge and Data Engineering, vol.16, issue.9, pp.1038-1051, 2004.
DOI : 10.1109/TKDE.2004.33

M. Kuramochi, Karypis : Grew-a scalable frequent subgraph discovery algorithm, ICDM icd, pp.439-442, 2004.

S. O. Kuznetsov, Learning of Simple Conceptual Graphs from Positive and Negative Examples, Lecture Notes in Computer Science, vol.1704, pp.384-391, 1999.
DOI : 10.1007/978-3-540-48247-5_47

P. Langley, Elements of Machine Learning, 1996.

C. Laurenço, Synthèse organique assistée par ordinateur, 1985.

C. Laurenço, Rapport d'activité du gdr 1093 du cnrs. traitement informatique de la connaissance en chimie organique, Rap. tech. CCIPE, 1998.

A. R. Leach and V. J. Gillet, An Introduction to Chemoinformatics, 2003.
DOI : 10.1007/978-1-4020-6291-9

N. Lesh, M. J. Zaki, and M. Ogihara, Scalable feature mining for sequential data, IEEE Intelligent Systems, vol.15, issue.2, pp.48-56, 2000.
DOI : 10.1109/5254.850827

URL : http://www.cs.rochester.edu/u/ogihara/research/KDD/scalable.ps.gz

M. Liquiere and J. Sallantin, Structural machine learning with galois lattice and graphs, pp.305-313, 1998.

H. Liu, H. Lu, L. Feng, and F. Hussain, Efficient Search of Reliable Exceptions, Lecture Notes in Computer Science, vol.1574, pp.194-203, 1999.
DOI : 10.1007/3-540-48912-6_27

D. J. Lubinsky, Discovery from databases : A review of ai and statistical techniques, Proceedings of IJCAI-89 Workshop on Knowledge Discovery in Databases, pp.204-218, 1989.

E. M. Luks, Isomorphism of graphs of bounded valence can be tested in polynomial time, Journal of Computer and System Sciences, vol.25, issue.1, pp.42-65, 1982.
DOI : 10.1016/0022-0000(82)90009-5

P. Mahé, N. Ueda, T. Akutsu, J. L. Perret, and J. P. , Graph Kernels for Molecular Structure???Activity Relationship Analysis with Support Vector Machines, Journal of Chemical Information and Modeling, vol.45, issue.4, pp.939-951, 2005.
DOI : 10.1021/ci050039t

H. Mannila and H. Toivonen, Discovering generalized episodes using minimal occurrences, KDD, pp.146-151, 1996.

H. Mannila and H. , Toivonen et A. I. Verkamo : Discovering frequent episodes in sequences, KDD, pp.210-215, 1995.

J. J. Mcgregor, Backtrack search algorithms and the maximal common subgraph problem, Software: Practice and Experience, vol.23, issue.1, pp.23-34, 1982.
DOI : 10.1145/362515.362562

B. D. Mckay, Practical graph isomorphism, II, Journal of Symbolic Computation, vol.60, pp.45-87, 1981.
DOI : 10.1016/j.jsc.2013.09.003

R. Michalski and R. Stepp, Automated Construction of Classifications: Conceptual Clustering Versus Numerical Taxonomy, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.5, issue.4, pp.396-410, 1983.
DOI : 10.1109/TPAMI.1983.4767409

R. S. Michalski, L. Kerschberg, K. A. Kaufman, and J. S. Ribeiro, Mining for knowledge in databases: The INLEN architecture, initial implementation and first results, Journal of Intelligent Information Systems, vol.13, issue.1, pp.85-113, 1992.
DOI : 10.1016/B978-0-444-87321-7.50010-7

T. Mitchell, Machine Learning, 1997.

T. M. Mitchell, Version spaces : A candidate elimination approach to rule learning, IJCAI, pp.305-310, 1977.

S. Muggleton, Inductive logic programming, ALT, pp.42-62, 1990.

S. Muggleton, Inductive logic programming, New Generation Computing, vol.7, issue.1, p.295, 1991.
DOI : 10.1007/978-3-642-96826-6

S. Muggleton, Inverse entailment and progol, New Generation Computing, vol.12, issue.1, pp.245-286, 1995.
DOI : 10.1080/09528139408953784

S. Muggleton, A. Srinivasan, R. D. King, and M. J. , Sternberg : Biochemical knowledge discovery using inductive logic programming, DS '98 : Proceedings of the First International Conference on Discovery Science, pp.326-341, 1998.

A. Napoli, ReprésentationsReprésentationsà objets et raisonnement par classification en intelligence artificielle, Thèse d'´ etat, 1992.

A. Napoli, Une introduction aux logiques de descriptions. Rap. tech. RR-3314, 1997.
URL : https://hal.archives-ouvertes.fr/inria-00073375

A. Napoli, C. Laurenço, and R. Ducournau, An object-based representation system for organic synthesis planning, International Journal of Human-Computer Studies, vol.41, issue.1-2, pp.5-32, 1994.
DOI : 10.1006/ijhc.1994.1051

R. T. Ng, L. V. Lakshmanan, and J. Han, Pang : Exploratory mining and pruning optimizations of constrained association rules, pp.13-24, 1998.

S. Nijssen and J. Kok, Efficient discovery of frequent unordered trees, Proceedings of the first International Workshop on Mining Graphs, 2003.

S. Nijssen and J. N. Kok, Faster association rules for multiple relations, pp.891-896, 2001.

S. Bibliographie, J. N. Nijssen, and . Kok, A quickstart in frequent structure mining can make a difference, Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.647-652, 2004.

E. R. Omiecinski, Alternative interest measures for mining associations in databases, IEEE Transactions on Knowledge and Data Engineering, vol.15, issue.1, pp.57-69, 2003.
DOI : 10.1109/TKDE.2003.1161582

M. Ott, Cheminformatics and organic chemistry. computer-assisted synthetic analysis, Cheminformatics, vol.1, issue.12, pp.83-109, 2004.

A. N. Papadopoulos, A. Lyritsis, and Y. Manolopoulos, SkyGraph: an algorithm for important subgraph discovery in relational graphs, Data Mining and Knowledge Discovery, vol.23, issue.13, pp.57-76, 2008.
DOI : 10.1017/CBO9780511815478

N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, Discovering Frequent Closed Itemsets for Association Rules, Proceedings of the 7th International Conference on Database Theory (ICDT'99), pp.398-416, 1999.
DOI : 10.1007/3-540-49257-7_25

URL : https://hal.archives-ouvertes.fr/hal-00467747

N. Pasquier, Y. Bastide, and R. Taouil, Lakhal : Efficient mining of association rules using closed itemset lattices, International Journal of Information Systems, vol.24, issue.1, pp.25-46, 1999.

N. Pasquier, R. Taouil, Y. Bastide, G. Stumme, and L. Lakhal, Generating a Condensed Representation for Association Rules, Journal of Intelligent Information Systems, vol.8, issue.6, pp.29-60, 2005.
DOI : 10.1007/s10844-005-0266-z

URL : https://hal.archives-ouvertes.fr/hal-00363015

J. Pei, J. Han, and L. V. Lakshmanan, Pushing Convertible Constraints in Frequent Itemset Mining, Data Mining and Knowledge Discovery, vol.8, issue.3, pp.227-252, 2004.
DOI : 10.1023/B:DAMI.0000023674.74932.4c

J. Pei, J. Han, B. Mortazavi-asl, J. Wang, H. Pinto et al., Mining sequential patterns by pattern-growth : The prefixspan approach, IEEE Trans. Knowl. Data Eng, issue.11, pp.161424-1440, 2004.

F. Pennerath and A. Napoli, La fouille de graphes dans les bases de données réactionnelles au service de la synthèse en chimie organique Ritschard et C. Djeraba, ´ eds : Extraction et gestion des connaissances, Actes dessixì emes journées Extraction et Gestion des Connaissances RNTI- E-6 de Revue des Nouvelles Technologies de l'Information, pp.17-20, 2006.

F. Pennerath and A. Napoli, Mining most informative subgraphs. Poster Session of Mining and Learning With Graphs, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00280088

F. Pennerath and A. Napoli, La famille des motifs les plus informatifs. applicationàapplicationà l'extraction de graphes en chimie organique, p.252, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00437092

F. Pennerath and A. Napoli, Leprobì eme de l'extraction des graphes d'intérêt maximal. applicationàapplicationà la fouille de réactions chimiques, eds : Actes du 16ème Congrès Francophone sur la Reconnaissance des Formes et l'Intelligence Artificielle, pp.133-142, 2008.

F. Pennerath, A. Napoli, W. Buntimes, M. Grobelnik, and J. Shawe-taylor, The Model of Most Informative Patterns and Its Application to Knowledge Extraction from Graph Databases, Lecture Notes in Artificial Intelligence, vol.17, issue.1, pp.205-220, 2009.
DOI : 10.1109/ICDM.2007.45

URL : https://hal.archives-ouvertes.fr/hal-00437536

F. Pennerath, G. Polaillon, and A. Napoli, A method for classifying vertices of labeled graphs applied to knowledge discovery from molecules, Proceedings of the 18th European Conference on Artificial Intelligence (ECAI'08), pp.147-151, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00344753

F. Pennerath, G. Polaillon, and A. Napoli, Mining Intervals of Graphs to Extract Characteristic Reaction Patterns, pp.210-221, 2008.
DOI : 10.1007/978-3-540-88411-8_21

URL : https://hal.archives-ouvertes.fr/hal-00344763

F. Pennerath, G. Polaillon, and A. Napoli, Prétraitement des bases de données de réactions chimiques pour la fouille de schémas de réactions Extraction et gestion des connaissances, Actes des 8` emes journées Extraction et Gestion des Connaissances, pp.547-558, 2008.

M. Pfoertner and M. Sitzmann, Computer-Assisted Synthesis Design by WODCA (CASD), Handbook of chemoinformatics, pp.1457-1507, 2003.
DOI : 10.1021/jo00127a044

G. Piatetsky-shapiro and W. Frawley, Knowledge discovery in databases, Menlo Park (Ca) and Cambridge (Ma), 1991.
DOI : 10.1145/846183.846197

G. Piatetsky-shapiro, Discovery, analysis, and presentation of strong rules, In Knowledge Discovery in Databases, pp.229-248, 1991.

G. Polaillon, Organisation et interprétation par les treillis de Galois de données de type multi-valué, intervalle ou histogramme, Thèse d'informatique, 1998.

G. Polaillon and E. G. Diday, Symbolic galois lattices of multivariate and interval tables, Proceedings of OSDA'97, 1997.

E. Proschak, J. K. Wegner, A. Schüller, G. Schneider, and U. Fechner, Molecular Query Language (MQL)A Context-Free Grammar for Substructure Matching, Journal of Chemical Information and Modeling, vol.47, issue.2, pp.295-301, 2007.
DOI : 10.1021/ci600305h

E. Prud-'hommeaux, Seaborne : Sparql query language for rdf, 2008.

J. R. Quinlan, C4.5 : programs for machine learning, 1993.

J. Quinlan, Induction of decision trees, Machine Learning, vol.1, issue.1, pp.81-106, 1986.
DOI : 10.1037/13135-000

L. D. Raedt, P. Idestam-almquist, and G. , Sablon : ?-subsumption for structural matching, Machine Learning : ECML97, Lecture Notes in Artificial Intelligence 1224, pp.73-84, 1997.

L. D. Bibliographie and . Raedt, Statistical relational learning : An inductive logic programming perspective, pp.3-5, 2005.

L. D. Raedt and S. Dzeroski, First-order jk-clausal theories are PAC-learnable, Artificial Intelligence, vol.70, issue.1-2, pp.375-392, 1994.
DOI : 10.1016/0004-3702(94)90112-0

L. D. Raedt, T. Guns, and S. Nijssen, Constraint programming for itemset mining, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 08, pp.204-212, 2008.
DOI : 10.1145/1401890.1401919

L. D. Raedt, M. Jaeger, and S. D. Lee, Mannila : A theory of inductive query answering, ICDM icd, pp.123-130, 2002.

C. Ra¨?ssira¨?ssi, T. Calders, and P. Poncelet, Mining Conjunctive Sequential Patterns, Lecture Notes in Computer Science, vol.5211, issue.1, p.19, 2008.
DOI : 10.1007/978-3-540-87479-9_17

J. Régin, O. Gascuel, C. Laurenço, F. Bernardi, and J. , Machine learning of strategic knowledge in organic synthesis from reaction databases, AIP Conference Proceedings, pp.618-623, 1995.
DOI : 10.1063/1.47873

J. Rissanen, Modeling by shortest data description, Automatica, vol.14, issue.5, pp.465-471, 1978.
DOI : 10.1016/0005-1098(78)90005-5

J. R. Rose and J. Gasteiger, HORACE: An automatic system for the hierarchical classification of chemical reactions, Journal of Chemical Information and Modeling, vol.34, issue.1, pp.74-90, 1994.
DOI : 10.1021/ci00017a010

U. Rückert, S. Kramer, H. Haddad, A. Omicini, R. L. Wainwright et al., Frequent free tree discovery in graph data, Proceedings of the 2004 ACM symposium on Applied computing , SAC '04, pp.564-570, 2004.
DOI : 10.1145/967900.968018

C. Ruecker, G. Ruecker, and S. H. Bertz, Organic Synthesis ??? Art or Science?, ChemInform, vol.44, issue.25, pp.378-386, 2004.
DOI : 10.1002/chin.200425218

S. J. Russell and P. Norvig, Artificial Intelligence : A Modern Approach, 2002.

D. C. Schmidt and L. E. , A Fast Backtracking Algorithm to Test Directed Graphs for Isomorphism Using Distance Matrices, Journal of the ACM, vol.23, issue.3, pp.433-445, 1976.
DOI : 10.1145/321958.321963

S. Sena, H. Agarwalb, and S. Senc, Chemical equation balancing: An integer programming approach, Mathematical and Computer Modelling, vol.44, issue.7-8, pp.678-691, 2006.
DOI : 10.1016/j.mcm.2006.02.004

E. Y. Shapiro, Algorithmic Program DeBugging, 1983.

A. Siebes, J. Vreeken, and M. Van-leeuwen, Item Sets That Compress, 2006.
DOI : 10.1137/1.9781611972764.35

R. F. Simmons, Storage and retrieval of aspects of meaning in directed graph structures, Communications of the ACM, vol.9, issue.3, pp.211-215, 1966.
DOI : 10.1145/365230.365268

A. Soulet, An Efficient Framework for Mining Flexible Constraints, Lecture Notes in Computer Science, vol.3518, pp.661-671, 2005.
DOI : 10.1007/11430919_76

URL : https://hal.archives-ouvertes.fr/hal-00324837

A. Soulet and B. Crémilleux, Extraction des top-k motifs par approximer-et-pousser ´ eds : Extraction et gestion des connaissances, Actes descinquì emes journées Extraction et Gestion des Connaissances RNTI-E-9 de Revue des Nouvelles Technologies de l'Information, pp.23-26, 2007.

A. Soulet and B. Crémilleux, Adequate condensed representations of patterns, Data Mining and Knowledge Discovery, vol.12, issue.3, pp.94-110, 2008.
DOI : 10.1007/s10618-008-0111-4

URL : https://hal.archives-ouvertes.fr/hal-01024051

J. F. Sowa, Conceptual Graphs for a Data Base Interface, IBM Journal of Research and Development, vol.20, issue.4, pp.336-357, 1976.
DOI : 10.1147/rd.204.0336

J. F. Sowa, Knowledge Representation : Logical, Philosophical, and Computational Foundations, 1999.

R. Srikant and R. Agrawal, Mining sequential patterns: Generalizations and performance improvements, Advances in Database Technology -EDBT'96, 5th International Conference on Extending Database Technology, pp.3-17, 1996.
DOI : 10.1007/BFb0014140

M. Stonebraker, R. Agrawal, U. Dayal, E. J. Neuhold, and A. Reuter, Dbms research at a crossroads : The vienna update, pp.688-692, 1993.

L. Szathmary, A. Napoli, and S. O. Kuznetsov, Zart : A multifunctional itemset mining algorithm, de CEUR Workshop Proceedings. CEUR-WS.org, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00001271

L. Szathmary, P. Valtchev, A. Napoli, and R. , Constructing Iceberg Lattices from Frequent Closures Using Generators, Boulicaut et, pp.136-147, 2008.
DOI : 10.1007/978-3-540-88411-8_15

URL : https://hal.archives-ouvertes.fr/inria-00331524

A. Tanaka, T. Kawai, T. Matsumoto, M. Fujii, T. Takabatake et al., Construction of a Statistical Evaluation Model Based on Molecular Centrality to Find Retrosynthetically Important Bonds in Organic Compounds, European Journal of Organic Chemistry, vol.106, issue.35, pp.5995-6007, 2008.
DOI : 10.1021/ci030415e

A. Tanaka, T. Kawaia, M. Fujiia, T. Matsumotoa, T. Takabatakea et al., Molecular centrality for synthetic design of convergent reactions, Tetrahedron, vol.64, issue.20, pp.4602-4612, 2008.
DOI : 10.1016/j.tet.2008.03.007

A. Termier, M. Rousset, and M. Sebag, TreeFinder: a first step towards XML data mining, 2002 IEEE International Conference on Data Mining, 2002. Proceedings., pp.450-457, 2002.
DOI : 10.1109/ICDM.2002.1183987

A. Termier, M. Rousset, and M. Sebag, DRYADE: A New Approach for Discovering Closed Frequent Trees in Heterogeneous Tree Databases, Fourth IEEE International Conference on Data Mining (ICDM'04), pp.543-546, 2004.
DOI : 10.1109/ICDM.2004.10078

A. Termier, M. Rousset, M. Sebag, K. Ohara, and T. Washio, DryadeParent, An Efficient and Robust Closed Attribute Tree Mining Algorithm, IEEE Transactions on Knowledge and Data Engineering, vol.20, issue.3, pp.300-320, 2008.
DOI : 10.1109/TKDE.2007.190695

URL : https://hal.archives-ouvertes.fr/inria-00292914

M. H. Todd, Computer-aided organic synthesis, Chemical Society Reviews, vol.412, issue.3, pp.247-66, 2005.
DOI : 10.1002/recl.19921110606

I. Ugi, J. Bauer, R. Baumgartner, E. Fontain, D. Forstmeyer et al., Computer assistance in the design of syntheses and a new generation of computer programs for the solution of chemical problems by molecular logic, Pure and Applied Chemistry, issue.11, pp.601573-1586, 1988.

I. Ugi, J. Bauer, C. Blomberger, J. Brandt, A. Dietz et al., Stein : Models, concepts, theories, and formal languages in chemistry and their use as a basis for computer assistance in chemistry, Journal of Chemical Information and Computer Sciences, vol.34, issue.1, pp.3-16, 1994.

M. Van-leeuwen and J. Vreeken, Siebes : Compression picks item sets that matter

N. Vanetik, S. E. Shimony, and E. Gudes, Support measures for graph data*, Data Mining and Knowledge Discovery, vol.13, issue.2, pp.243-260, 2006.
DOI : 10.1007/s10618-006-0044-8

P. Vismara, P. Jambaud, C. Laurenço, and J. Quinqueton, Resyn : objets, classification et raisonnement distribué en chimie organique, LangagesàLangagesà objets : ´ etat et perspectives de la recherche, pp.397-419, 1998.

P. Vismara, Reconnaissance et représentation d'´ eléments structuraux pour la description d'objets complexes. ApplicationàApplicationà l'´ elaboration de stratégies de synthèse en chimie organique, Thèse d'informatique, Université des Sciences et Techniques du Languedoc, 1995.

P. Vismara and C. Laurenço, An abstract representation for molecular graphs, DIMACS Series in Discrete Mathematics and Theoretical Computer Science, vol.51, pp.343-366, 2000.
DOI : 10.1090/dimacs/051/26

P. Vismara, J. Régin, J. Quinqueton, M. Py, and C. Laurenço, Lapied : Resyn ? un système d'aidè a la conception de plans de synthèse en chimie organique, Actes des 12èmes Journeés Internationales Intelligence Artificielle, Systèmes Experts, pp.305-318, 1992.

G. E. Vleduts, Concerning one system of classification and codification of organic reactions, Information Storage and Retrieval, vol.1, issue.2-3, pp.117-146, 1963.
DOI : 10.1016/0020-0271(63)90013-5

C. Wang, W. Wang, J. Pei, Y. Zhu, and B. Shi, Scalable mining of large disk-based graph databases, Proceedings of the 2004 ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '04, pp.316-325, 2004.
DOI : 10.1145/1014052.1014088

C. Wang, Y. Zhu, T. Wu, W. Wang, B. Shi-zhang et al., Constraint-Based Graph Mining in Large Database, de Lecture Notes in Computer Science, pp.133-144, 2005.
DOI : 10.1007/978-3-540-31849-1_14

J. Wang, J. Han, Y. Lu, and P. Tzvetkov, TFP: an efficient algorithm for mining top-k frequent closed itemsets, IEEE Transactions on Knowledge and Data Engineering, vol.17, issue.5, pp.652-664, 2005.
DOI : 10.1109/TKDE.2005.81

K. Wang, Y. Jiang, J. X. Yu, G. Dong, and J. Han, Pushing aggregate constraints by divide-and-approximate, Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405), pp.291-302, 2003.
DOI : 10.1109/ICDE.2003.1260800

K. Wang and H. Liu, Discovering typical structures of documents, Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval , SIGIR '98, pp.146-154, 1998.
DOI : 10.1145/290941.290982

K. Wang and H. Liu, Discovering structural association of semistructured data, IEEE Transactions on Knowledge and Data Engineering, vol.12, issue.3, pp.353-371, 2000.
DOI : 10.1109/69.846290

K. Wang, L. Wang, Q. Yuan, S. Luo, J. Yao et al., Construction of a generic reaction knowledge base by reaction data mining, Journal of Molecular Graphics and Modelling, vol.19, issue.5, pp.427-433, 2001.
DOI : 10.1016/S1093-3263(00)00102-9

T. Washio, E. Suzuki, K. M. Ting, and A. Inokuchi, Advances in Knowledge Discovery and Data Mining, 12th Pacific-Asia Conference, Proceedings, 2008.

D. Weininger, SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules, Journal of Chemical Information and Modeling, vol.28, issue.1, pp.31-36, 1988.
DOI : 10.1021/ci00057a005

C. Bibliographie, R. Wilcox, and . Levinson, A self-organized knowledge base for recall, design, and discovery in organic chemistry, Artificial Intelligence Applications in Chemistry ACS Symposium Series, vol.306, pp.209-230, 1986.

R. Wille, Restructuring lattice theory : An approach based on hierarchies of concepts, Ordered Sets, pp.445-470, 1982.

R. Wille, Knowledge acquisition by methods of formal concept analysis, Proceedings of the Conference on Data Analysis, Learning Symbolic and Numeric Knowledge, pp.365-380, 1989.

W. Wipke, W. Wipke, S. Heller, R. Feldman, and E. Hyde, Computer?assisted three?dimensional synthetic analysis Computer Representation and Manipulation of Chemical Information, pp.147-174, 1974.

M. Wörlein, T. Meinl, I. Fischer, and M. , Philippsen : A quantitative comparison of the subgraph miners mofa, gspan, ffsm, and gaston, pp.392-403, 2005.

Y. Xiao, J. Yao, Z. Li, and M. H. Dunham, Efficient data mining for maximal frequent subtrees, Third IEEE International Conference on Data Mining, pp.379-386, 2003.
DOI : 10.1109/ICDM.2003.1250943

D. Xin, H. Cheng, X. Yan, and J. Han, Extracting redundancy-aware top-k patterns, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '06, pp.444-453
DOI : 10.1145/1150402.1150452

D. Xin, J. Han, X. Yan, H. Cheng, K. Böhm et al., Mining compressed frequent-pattern sets, Proceedings of the 31st International Conference on Very Large Data Bases, pp.709-720, 2005.

X. Yan, H. Cheng, J. Han, and P. S. Yu, Mining significant graph patterns by leap search, Proceedings of the 2008 ACM SIGMOD international conference on Management of data , SIGMOD '08, pp.433-444, 2008.
DOI : 10.1145/1376616.1376662

URL : http://www.cs.uiuc.edu/homes/hanj/pdf/sigmod08_xifengyan.pdf

X. Yan and J. , Han : gspan : Graph-based substructure pattern mining, Proceedings of the 2002 IEEE International Conference on Data Mining, pp.9-12, 2002.

X. Yan, J. Han, and R. Afshar, CloSpan: Mining: Closed Sequential Patterns in Large Datasets, 2003.
DOI : 10.1137/1.9781611972733.15

X. Yan, P. S. Yu, and J. Han, Graph indexing, Proceedings of the 2004 ACM SIGMOD international conference on Management of data , SIGMOD '04, pp.335-346, 2004.
DOI : 10.1145/1007568.1007607

X. Yan, P. S. Yu, and J. Han, Graph indexing based on discriminative frequent structure analysis, ACM Transactions on Database Systems, vol.30, issue.4, pp.960-993, 2005.
DOI : 10.1145/1114244.1114248

X. Yan, X. J. Zhou, J. Han, R. Grossman, R. J. Bayardo et al., Mining closed relational graphs with connectivity constraints, Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining , KDD '05, pp.324-333, 2005.
DOI : 10.1145/1081870.1081908

K. Yoshida and H. Motoda, Graph-based induction as a unified learning framework, Applied Intelligence, vol.42, issue.2, pp.297-328, 1994.
DOI : 10.1007/BF00872095

K. Yoshida and H. Motoda, CLIP: concept learning from inference patterns, Artificial Intelligence, vol.75, issue.1, pp.63-92, 1995.
DOI : 10.1016/0004-3702(94)00066-A

M. Zaki and C. Hsiao, CHARM: An Efficient Algorithm for Closed Itemset Mining, Second SIAM International Conference on Data Mining, 2002.
DOI : 10.1137/1.9781611972726.27

M. J. Zaki, Scalable algorithms for association mining, IEEE Transactions on Knowledge and Data Engineering, vol.12, issue.3, pp.372-390, 2000.
DOI : 10.1109/69.846291

M. J. Zaki, Spade : An efficient algorithm for mining frequent sequences, Machine Learning, vol.42, issue.1/2, pp.31-60, 2001.
DOI : 10.1023/A:1007652502315

M. J. Zaki, Efficiently mining frequent trees in a forest, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '02, pp.71-80, 2002.
DOI : 10.1145/775047.775058

M. J. Zaki, Efficiently mining frequent embedded unordered trees, Fundam. Inform, vol.66, issue.12, pp.33-52, 2005.

M. J. Zaki, Efficiently mining frequent trees in a forest: algorithms and applications, IEEE Transactions on Knowledge and Data Engineering, vol.17, issue.8, pp.1021-1035, 2005.
DOI : 10.1109/TKDE.2005.125

M. J. Zaki, N. Parimi, N. De, F. Gao, B. Phoophakdee et al., Towards generic pattern mining eds : Formal Concept Analysis, Third International Conference Proceedings, pp.1-20, 2005.

M. J. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, New algorithms for fast discovery of association rules, KDD, pp.283-286, 1997.

Z. Zeng, J. Wang, and L. , Efficient Mining of Minimal Distinguishing Subgraph Patterns from Graph Databases, Washio et, pp.1062-1068, 2008.
DOI : 10.1007/978-3-540-68125-0_114

Q. Zhang, J. Aires-de, and . Sousa, Structure-Based Classification of Chemical Reactions without Assignment of Reaction Centers, Journal of Chemical Information and Modeling, vol.45, issue.6, pp.1775-1783, 2005.
DOI : 10.1021/ci0502707

A. Zhou, W. Jin, S. Zhou, and Z. Tian, Incremental Mining of Schema for Semistructured Data, Lecture Notes in Computer Science, vol.1574, pp.159-168, 1999.
DOI : 10.1007/3-540-48912-6_22

F. Zhu, X. Yan, J. Han, and P. S. Yu, gPrune: A Constraint Pushing Framework for Graph Pattern Mining, Lecture Notes in Computer Science, vol.4426, pp.388-400, 2007.
DOI : 10.1007/978-3-540-71701-0_38