, Découverte de sous-groupes avec les arbres de recherche de
, 17èmes Journées Francophones Extraction et Gestion des Connaissances, vol.24, pp.273-284, 2017.
A Proposition for Sequence Mining Using Pattern Structures, Formal Concept Analysis-14th International Conference, vol.10308, p.2017, 1316. ,
URL : https://hal.archives-ouvertes.fr/hal-01549107
, Local Subgroup Discovery for Eliciting and Understanding New Structure-Odor Relationships
URL : https://hal.archives-ouvertes.fr/hal-01346660
, Discovery Science-19th International Conference, vol.9956, pp.19-34, 2016.
Kaytoue: h(odor): Interactive Discovery of Hypotheses on the Structure-Odor ,
, Machine Learning and Knowledge Discovery in Databases-European Conference, ECML PKDD 2016, vol.9853, pp.17-21, 2016.
Boulicaut: Vers la découverte de modèles exceptionnels locaux : des règles descriptives liant les molécules à leurs odeurs, 15èmes Journées Francophones Extraction et Gestion des Connaissances, EGC, vol.26, pp.305-316, 2015. ,
Kaytoue: A Pattern Mining Approach to Study Strategy Balance in RTS Games, IEEE Transactions on Computational Intelligence and AI in Games, vol.9, issue.2, pp.123-132 ,
, Mining Balanced Sequential Patterns in RTS Games
URL : https://hal.archives-ouvertes.fr/hal-01100933
, Including Prestigious Applications of Intelligent Systems, European Conference on Artificial Intelligence, vol.263, pp.975-976, 2014.
,
,
, , vol.506
, , vol.875
,
Expected-outcome: A general model of static evaluation, IEEE Trans. Pattern Anal. Mach. Intell, vol.12, issue.2, pp.182-193, 1990. ,
Evaluation measures for multi-class subgroup discovery, ECML/PKDD, Part I, pp.35-50, 2009. ,
Data Mining-The Textbook, 2015. ,
Mining association rules between sets of items in large databases, ACM SIGMOD, pp.207-216, 1993. ,
Fast algorithms for mining association rules in large databases, VLDB, pp.487-499, 1994. ,
Mining sequential patterns, IEEE ICDE, pp.3-14, 1995. ,
A partial-closure canonicity test to increase the efficiency of cbo-type algorithms, ICCS, pp.37-50, 2014. ,
A 'best-of-breed' approach for designing a fast algorithm for computing fixpoints of galois connections, Inf. Sci, vol.295, pp.633-649, 2015. ,
Perfume and flavor materials of natural origin, vol.2, 1994. ,
VIKAMINE-open-source subgroup discovery, pattern mining, and analytics, ECML/PKDD, pp.842-845, 2012. ,
Fast subgroup discovery for continuous target concepts, ISMIS, pp.35-44, 2009. ,
SD-map-A fast algorithm for exhaustive subgroup discovery, PKDD, pp.6-17, 2006. ,
Finite-time analysis of the multiarmed bandit problem, Machine Learning, vol.47, pp.235-256, 2002. ,
Handbook of evolutionary computation. Release, vol.97, p.1, 1997. ,
Detecting group differences: Mining contrast sets, Data Min. Knowl. Discov, vol.5, issue.3, pp.213-246, 2001. ,
Mining convex polygon patterns with formal concept analysis, IJCAI, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01573841
Unsupervised exceptional attributed subgraph mining in urban data, IEEE ICDM, pp.1-12, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01430622
A survey of clustering data mining techniques, Grouping Multidimensional Data-Recent Advances in Clustering, pp.25-71, 2006. ,
Multiobjective evolutionary induction of subgroup discovery fuzzy rules: A case study in marketing, Advances in Data Mining, Applications in Medicine, Web Mining, Marketing, Image and Signal Mining, co-located with 6th Industrial Conference on Data Mining, pp.337-349, 2006. ,
Mining graph evolution rules, ECML/PKDD, Part I, pp.115-130, 2009. ,
Constraint-based concept mining and its application to microarray data analysis, Intell. Data Anal, pp.59-82, 2005. ,
URL : https://hal.archives-ouvertes.fr/hal-01535568
Cadiaplayer: A simulation-based general game player, IEEE Trans. Comput. Intellig. and AI in Games, vol.1, issue.1, pp.4-15, 2009. ,
Direct local pattern sampling by efficient two-step random procedures, ACM SIGKDD, pp.582-590, 2011. ,
Découverte de sous-groupes avec les arbres de recherche de monte carlo, EGC, pp.23-37, 2017. ,
Local subgroup discovery for eliciting and understanding new structure-odor relationships, DS, pp.19-34, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01346660
Vers la découverte de modèles exceptionnels locaux : des règles descriptives liant les molécules à leurs odeurs, EGC, pp.305-316, 2015. ,
Fouille de motifs séquentiels pour l'élicitation de stratégies à partir de traces d'interactions entre agents en compétition, EGC, pp.359-370, 2014. ,
Mining balanced sequential patterns in RTS games, ECAI, pp.975-976, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-01100933
h(odor): Interactive discovery of hypotheses on the structure-odor relationship in neuroscience, Machine Learning and Knowledge Discovery in Databases-European Conference, ECML PKDD, Part III, pp.17-21, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01346679
Any-time diverse subgroup discovery with monte carlo tree search. Major revision in Data Min, Knowl. Discov. journal, 2017. ,
A pattern mining approach to study strategy balance in RTS games, IEEE Trans. Comput. Intellig. and AI in Games, vol.9, issue.2, pp.123-132, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01252728
, Constraint-Based Mining and Inductive Databases, vol.3848, 2005.
URL : https://hal.archives-ouvertes.fr/hal-01613479
Constraint-based data mining, Data Mining and Knowledge Discovery Handbook, pp.339-354, 2010. ,
URL : https://hal.archives-ouvertes.fr/ujm-00374308
The process of knowledge discovery in databases, Advances in Knowledge Discovery and Data Mining, pp.37-57, 1996. ,
One in a million: picking the right patterns, Knowl. Inf. Syst, vol.18, issue.1, pp.61-81, 2009. ,
A survey of monte carlo tree search methods, IEEE Trans. Comput. Intellig. and AI in Games, vol.4, issue.1, pp.1-43, 2012. ,
A novel multigene family may encode odorant receptors: a molecular basis for odor recognition, Cell, vol.65, issue.1, pp.175-187, 1991. ,
MAFIA: A maximal frequent itemset algorithm for transactional databases, ICDE, pp.443-452, 2001. ,
A survey on condensed representations for frequent sets, Constraint-Based Mining and Inductive Databases, European Workshop on Inductive Databases and Constraint Based Mining, pp.64-80, 2004. ,
URL : https://hal.archives-ouvertes.fr/hal-01613469
NMEEF-SD: non-dominated multiobjective evolutionary algorithm for extracting fuzzy rules in subgroup discovery, IEEE Trans. Fuzzy Systems, vol.18, issue.5, pp.958-970, 2010. ,
Categorical dimensions of human odor descriptor space revealed by non-negative matrix factorization, PLOS ONE, vol.8, issue.9, p.2013 ,
Generalized rapid action value estimation, IJCAI, pp.754-760, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01436522
On the parallelization of uct, proceedings of the Computer Games Workshop, pp.93-101, 2007. ,
Closed and noise-tolerant patterns in n-ary relations, Data Min. Knowl. Discov, vol.26, issue.3, pp.574-619, 2013. ,
Progressive strategies for monte-carlo tree search, JCIS, pp.655-661, 2007. ,
Parallel monte-carlo tree search, CG, pp.60-71, 2008. ,
DOI : 10.1007/978-3-540-87608-3_6
A proposition for sequence mining using pattern structures, vol.ICFCA, pp.106-121, 2017. ,
DOI : 10.1007/978-3-319-59271-8_7
URL : https://hal.archives-ouvertes.fr/hal-01549107
Mining graph data, 2006. ,
Computing "elo ratings" of move patterns in the game of go, ICGA Journal, vol.30, issue.4, pp.198-208, 2007. ,
URL : https://hal.archives-ouvertes.fr/inria-00149859
Structure-odour relationships reviewed in the postgenomic era, Flavour and Fragrance Journal, vol.30, issue.5, pp.342-361, 2015. ,
A theory of inductive query answering, IEEE ICDM, pp.123-130, 2002. ,
Exceptional preferences mining, DS, pp.3-18, 2016. ,
A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evolutionary Computation, vol.6, issue.2, pp.182-197, 2002. ,
DOI : 10.1109/4235.996017
Evolutionary fuzzy rule induction process for subgroup discovery: A case study in marketing, IEEE Trans. Fuzzy Systems, vol.15, issue.4, pp.578-592, 2007. ,
Structure-odor relationships of semisynthetic ?-santalol analogs, Chemistry & Biodiversity, vol.11, issue.11, pp.1843-1860, 2014. ,
DOI : 10.1002/cbdv.201400082
Efficient mining of emerging patterns: Discovering trends and differences, ACM SIGKDD, pp.43-52, 1999. ,
DOI : 10.1145/312129.312191
Exceptionally monotone models-the rank correlation model class for exceptional model mining, IEEE ICDM, pp.111-120, 2015. ,
Different slopes for different folks: mining for exceptional regression models with cook's distance, ACM SIGKDD, pp.868-876, 2012. ,
Exceptional model mining-supervised descriptive local pattern mining with complex target concepts, Data Min. Knowl. Discov, vol.30, issue.1, pp.47-98, 2016. ,
Exploiting false discoveries-statistical validation of patterns and quality measures in subgroup discovery, IEEE ICDM, pp.151-160, 2011. ,
Subgroup discovery meets bayesian networks-an exceptional model mining approach, IEEE ICDM, pp.158-167, 2010. ,
DOI : 10.1109/icdm.2010.53
Inductive Databases and Constraint-Based Data Mining, 2010. ,
Interactive discovery of interesting subgroup sets, IDA, pp.150-161, 2013. ,
Interactive learning of pattern rankings, International Journal on Artificial Intelligence Tools, vol.23, issue.6, 2014. ,
Dream olfaction prediction challenge, Sponsors: IFF, IBM Research, Sage Bionetworks and DREAM. URL: www.synapse.org/#!Synapse, p.2811262, 2015. ,
Multi-interval discretization of continuous-valued attributes for classification learning, IJCAI, pp.1022-1029, 1993. ,
From data mining to knowledge discovery: An overview, Advances in Knowledge Discovery and Data Mining, pp.1-34, 1996. ,
Foundations of Rule Learning. Cognitive Technologies, 2012. ,
Expert-guided subgroup discovery: Methodology and application, J. Artif. Intell. Res, vol.17, pp.501-527, 2002. ,
Formal concept analysis-mathematical foundations, 1999. ,
Feature selection as a one-player game, IEEE ICDM, pp.359-366, 2010. ,
URL : https://hal.archives-ouvertes.fr/inria-00484049
A contribution to Reinforcement Learning; application to Computer-Go, 2007. ,
Combining online and offline knowledge in UCT, ICML 2007, pp.273-280, 2007. ,
URL : https://hal.archives-ouvertes.fr/inria-00164003
20 years of pattern mining: a bibliometric survey, SIGKDD Explorations, vol.15, issue.1, pp.41-50, 2013. ,
Efficiently mining maximal frequent itemsets, IEEE ICDM, pp.163-170, 2001. ,
On subgroup discovery in numerical domains, Data Min. Knowl. Discov, vol.19, issue.2, pp.210-226, 2009. ,
Tight optimistic estimates for fast subgroup discovery, ECML/PKDD, pp.440-456, 2008. ,
An introduction to variable and feature selection, Journal of Machine Learning Research, vol.3, pp.1157-1182, 2003. ,
Frequent pattern mining: current status and future directions, Data Min. Knowl. Discov, vol.15, issue.1, pp.55-86, 2007. ,
Mining frequent patterns without candidate generation, ACM SIGMOD, pp.1-12, 2000. ,
Trends & controversies: Support vector machines, IEEE Intelligent Systems, vol.13, issue.4, pp.18-28, 1998. ,
All-moves-as-first heuristics in monte-carlo go, ICAI, pp.605-610, 2009. ,
Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence, 1975. ,
Optimization of association rule mining queries, Intell. Data Anal, pp.341-357, 2002. ,
Physicochemical influence on odor hedonics: Where does it occur first?, Communicative & integrative biology, vol.4, issue.5, pp.563-565, 2011. ,
Odor classification: a review of factors influencing perceptionbased odor arrangements. Chemical senses, vol.38, pp.189-209, 2013. ,
APRIORI-SD: adapting association rule learning to subgroup discovery, IDA, pp.230-241, 2003. ,
Revisiting numerical pattern mining with formal concept analysis, IJCAI, pp.1342-1347, 2011. ,
URL : https://hal.archives-ouvertes.fr/inria-00600222
Mining gene expression data with pattern structures in formal concept analysis, Inf. Sci, vol.181, issue.10, 1989. ,
URL : https://hal.archives-ouvertes.fr/hal-00541100
Exceptional contextual subgraph mining, Machine Learning, pp.1-41, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01488732
Predicting human olfactory perception from chemical features of odor molecules, Science, vol.355, issue.6327, pp.820-826, 2017. ,
Predicting odor pleasantness from odorant structure: pleasantness as a reflection of the physical world, The Journal of Neuroscience, vol.27, issue.37, pp.10015-10023, 2007. ,
Explora: A multipattern and multistrategy discovery assistant, Advances in Knowledge Discovery and Data Mining, pp.249-271, 1996. ,
Census data mining, an application, PKDD, pp.65-79, 2002. ,
Bandit based monte-carlo planning, ECML, pp.282-293, 2006. ,
Cost-based quality measures in subgroup discovery, J. Intell. Inf. Syst, vol.45, issue.3, pp.337-355, 2015. ,
Advances in algorithms based on CbO, CLA, pp.325-337, 2010. ,
A new benchmark for artificial intelligence, Commun. ACM, vol.54, issue.8, pp.13-15, 2011. ,
A fast algorithm for computing all intersections of objects from an arbitrary semilattice. Nauchno-Tekhnicheskaya Informatsiya Seriya 2-Informatsionnye protsessy i sistemy, vol.2, pp.17-20, 1993. ,
A fast algorithm for computing all intersections of objects in a finite semilattice. Automatic Documentation and Mathematical Linguistics, vol.27, pp.400-412, 1993. ,
Decision support through subgroup discovery: Three case studies and the lessons learned, Machine Learning, vol.57, pp.115-143, 2004. ,
Rule evaluation measures: A unifying view, ILP, pp.174-185, 1999. ,
Deep learning, Nature, vol.521, issue.7553, pp.436-444, 2015. ,
Exceptional model mining, ECML/PKDD, pp.1-16, 2008. ,
Fast exhaustive subgroup discovery with numerical target concepts, Data Min. Knowl. Discov, vol.30, issue.3, pp.711-762, 2016. ,
DOI : 10.1007/s10618-015-0436-8
Generic pattern trees for exhaustive exceptional model mining, ECML PKDD, pp.277-292, 2012. ,
DOI : 10.1007/978-3-642-33486-3_18
URL : https://link.springer.com/content/pdf/10.1007%2F978-3-642-33486-3_18.pdf
Fast discovery of relevant subgroup patterns, FLAIRS, pp.428-433, 2010. ,
The HARPY speech recognition system, 1976. ,
DOI : 10.1121/1.2003013
URL : https://asa.scitation.org/doi/pdf/10.1121/1.2003013
Learning rules for multi-label classification: a stacking and a separate-and-conquer approach, Machine Learning, vol.105, pp.77-126, 2016. ,
Discovering subgroups by means of genetic programming, EuroGP, pp.121-132, 2013. ,
DOI : 10.1007/978-3-642-37207-0_11
Levelwise search and borders of theories in knowledge discovery, Data Min. Knowl. Discov, vol.1, issue.3, pp.241-258, 1997. ,
Rocsearch-an roc-guided search strategy for subgroup discovery, IEEE ICDM, pp.704-712, 2014. ,
DOI : 10.1137/1.9781611973440.81
URL : http://ceur-ws.org/Vol-1226/paper29.pdf
The molecular basis of olfactory chemoreception, Angewandte Chemie International Edition, vol.43, issue.47, pp.6410-6412, 2004. ,
Instant exceptional model mining using weighted controlled pattern sampling, IDA, pp.203-214, 2014. ,
DOI : 10.1007/978-3-319-12571-8_18
Traversing itemset lattice with statistical metric pruning, ACM PODS, pp.226-236, 2000. ,
DOI : 10.1145/335168.335226
Mining cohesive patterns from graphs with feature vectors, SIAM SDM, pp.593-604, 2009. ,
DOI : 10.1137/1.9781611972795.51
URL : https://epubs.siam.org/doi/pdf/10.1137/1.9781611972795.51
Subgroup discovery for test selection: A novel approach and its application to breast cancer diagnosis, IDA, pp.119-130, 2009. ,
Constraint-based pattern mining, Frequent Pattern Mining, pp.147-163, 2014. ,
DOI : 10.1007/978-3-319-07821-2_7
Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining, Journal of Machine Learning Research, vol.10, pp.377-403, 2009. ,
Multi-objective evolutionary approach for subgroup discovery, Hybrid Artificial Intelligent Systems, pp.271-278, 2011. ,
Efficient mining of association rules using closed itemset lattices, Inf. Syst, vol.24, issue.1, pp.25-46, 1999. ,
DOI : 10.1016/s0306-4379(99)00003-4
Prefixspan: Mining sequential patterns by prefix-projected growth, IEEE ICDE, pp.215-224, 2001. ,
Turning cartwheels: an alternating algorithm for mining redescriptions, ACM KDD, pp.266-275, 2004. ,
Constraint-based pattern mining in dynamic graphs, IEEE ICDM, pp.950-955, 2009. ,
URL : https://hal.archives-ouvertes.fr/hal-01437815
Searching for rules to detect defective modules: A subgroup discovery approach, Inf. Sci, vol.191, pp.14-30, 2012. ,
Artificial Intelligence-A Modern Approach (3. internat. ed.). Pearson Education, 2010. ,
A survey of decision tree classifier methodology, IEEE Trans. Systems, Man, and Cybernetics, vol.21, issue.3, pp.660-674, 1991. ,
Single-player monte-carlo tree search, CG, vol.5131, pp.1-12, 2008. ,
DOI : 10.1007/978-3-540-87608-3_1
,
Mastering the game of go with deep neural networks and tree search, Nature, vol.529, issue.7587, pp.484-489, 2016. ,
Mining dominant patterns in the sky, IEEE ICDM, pp.655-664, 2011. ,
DOI : 10.1109/icdm.2011.100
URL : https://hal.archives-ouvertes.fr/inria-00623566
Computing iceberg concept lattices with Titanic, Data Knowl. Eng, vol.42, issue.2, pp.189-222, 2002. ,
URL : https://hal.archives-ouvertes.fr/hal-00578830
Least squares support vector machine classifiers, Neural Processing Letters, vol.9, issue.3, pp.293-300, 1999. ,
Introduction to Data Mining, 2005. ,
Mining multi-label data, Data Mining and Knowledge Discovery Handbook, pp.667-685, 2010. ,
DOI : 10.1007/978-0-387-09823-4_34
Mulan: A java library for multi-label learning, Journal of Machine Learning Research, vol.12, pp.2411-2414, 2011. ,
Addintent: A new incremental algorithm for constructing concept lattices, ICFCA, pp.372-385, 2004. ,
Association discovery in two-view data, IEEE Trans. Knowl. Data Eng, vol.27, issue.12, pp.3190-3202, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01242988
Non-redundant subgroup discovery in large and complex data, ECML/PKDD, pp.459-474, 2011. ,
Diverse subgroup set discovery, Data Min. Knowl. Discov, vol.25, issue.2, pp.208-242, 2012. ,
Discovering skylines of subgroup sets, ECML/PKDD, pp.272-287, 2013. ,
Data mining: practical machine learning tools and techniques, 2011. ,
An algorithm for multi-relational discovery of subgroups, PKDD, pp.78-87, 1997. ,
DOI : 10.1007/3-540-63223-9_108
CHARM: an efficient algorithm for closed itemset mining, SIAM SDM, pp.457-473, 2002. ,
DOI : 10.1137/1.9781611972726.27
Data Mining and Analysis: Fundamental Concepts and Algorithms, 2014. ,
Spea2: Improving the strength pareto evolutionary algorithm, Eurogen, vol.3242, pp.95-100, 2001. ,