Depth first generation of long patterns, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '00, pp.108-118, 2000. ,
DOI : 10.1145/347090.347114
Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD international conference on Management of data, SIGMOD '93, pp.207-216, 1993. ,
Fast discovery of association rules, Advances in Knowledge Discovery and Data Mining, pp.307-328, 1996. ,
Fast algorithms for mining association rules in large databases, Proceedings of the 20th International Conference on Very Large Data Bases, VLDB '94, pp.487-499, 1994. ,
RTM: Laws and a Recursive Generator for Weighted Time-Evolving Graphs, 2008 Eighth IEEE International Conference on Data Mining, pp.701-706, 2008. ,
DOI : 10.1109/ICDM.2008.123
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.150.4326
Mining Positive and Negative Association Rules: An Approach for Confined Rules, Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, ECML PKDD '04, pp.27-38, 2004. ,
DOI : 10.1007/978-3-540-30116-5_6
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.2.4218
Group formation in large social networks, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '06, pp.44-54, 2006. ,
DOI : 10.1145/1150402.1150412
Redundancy, deduction schemes, and minimum-size bases for association rules, Logical Methods in Computer Science, vol.6, issue.2, p.2010 ,
Designing templates for mining association rules, Journal of Intelligent Information Systems, vol.9, issue.1, pp.7-32, 1997. ,
DOI : 10.1023/A:1008637019359
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
Detecting change in categorical data: mining contrast sets, Proceedings of the 5th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '99, pp.302-306 ,
Mining Graph Evolution Rules, ECML/PKDD, pp.115-130, 2009. ,
DOI : 10.1007/978-3-540-71701-0_38
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.184.2851
As Time Goes by: Discovering Eras in Evolving Social Networks, Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining, PAKDD '10, pp.81-90, 2010. ,
DOI : 10.1007/978-3-642-13657-3_11
Découvertes de motifs pertinents pour l'analyse du transcriptome: applicationàapplicationà l'insulino-résistance, 2005. ,
Constraint-based formal concept mining and its application to microarray data analysis, Intelligent Data Analysis, vol.9, issue.1, pp.59-82, 2005. ,
Pushing Tougher Constraints in Frequent Pattern Mining, Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD '05, pp.114-124, 2005. ,
DOI : 10.1007/11430919_15
On condensed representations of constrained frequent patterns, Knowledge and Information Systems, vol.9, issue.2, pp.180-201, 2006. ,
DOI : 10.1007/s10115-005-0201-1
Extending the state-of-the-art of constraint-based pattern discovery, Data & Knowledge Engineering, vol.60, issue.2, pp.377-399, 2007. ,
DOI : 10.1016/j.datak.2006.02.006
Pattern Mining in Frequent Dynamic Subgraphs, Sixth International Conference on Data Mining (ICDM'06), pp.818-822, 2006. ,
DOI : 10.1109/ICDM.2006.124
Inductive Databases and Multiple Uses of Frequent Itemsets: The cInQ Approach, Database Support for Data Mining Applications, pp.1-23, 2004. ,
DOI : 10.1109/69.149926
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
Approximation of frequency queris by means of free-sets, Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, PKDD '00, pp.75-85, 2000. ,
Mining free itemsets under constraints, Proceedings 2001 International Database Engineering and Applications Symposium, pp.322-329, 2001. ,
DOI : 10.1109/IDEAS.2001.938100
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.104.664
Dynamic itemset counting and implication rules for market basket data, ACM SIGMOD Record, vol.26, issue.2, pp.255-264, 1997. ,
DOI : 10.1145/253262.253325
What is frequent in a single graph? In Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining, PAKDD '08, pp.858-863, 2008. ,
MAFIA: a maximal frequent itemset algorithm for transactional databases, Proceedings 17th International Conference on Data Engineering, pp.443-452, 2001. ,
DOI : 10.1109/ICDE.2001.914857
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.100.6805
Condensed Representations of Frequent Itemsets: Application to Descriptive Pattern Discovery, 2002. ,
Anti-monotonic Overlap-Graph Support Measures, 2008 Eighth IEEE International Conference on Data Mining, pp.73-82, 2008. ,
DOI : 10.1109/ICDM.2008.114
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.422.8667
A Survey on Condensed Representations for Frequent Sets, Constraint Based Mining and Inductive Databases, pp.64-80, 2005. ,
DOI : 10.1007/11615576_4
Constraint-based mining of closed patterns in noisy n-ary relations, L'Institut National des Sicenses Appliquées de Lyon, 2010. ,
URL : https://hal.archives-ouvertes.fr/tel-00508534
-ary Relations, Proceedings of the Eighth SIAM International Conference on Data Mining, SDM '08, pp.37-48, 2008. ,
DOI : 10.1137/1.9781611972788.4
URL : https://hal.archives-ouvertes.fr/hal-01408836
-ary relations, ACM Transactions on Knowledge Discovery from Data, vol.3, issue.1, pp.1-36, 2009. ,
DOI : 10.1145/1497577.1497580
URL : https://hal.archives-ouvertes.fr/hal-01408836
Mining Constrained Cross-Graph Cliques in Dynamic Networks, Inductive Databases and Constraint-based Data Mining, pp.199-228, 2010. ,
DOI : 10.1007/978-1-4419-7738-0_9
URL : https://hal.archives-ouvertes.fr/hal-01381541
Structural and temporal analysis of the blogosphere through community factorization, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '07, pp.163-172, 2007. ,
DOI : 10.1145/1281192.1281213
Mining association rules in multiple relations, Inductive Logic Programming, 7th International Workshop, pp.125-132, 1997. ,
DOI : 10.1007/3540635149_40
An efficient computation of frequent queries in a star schema, Proceedings of the 21st international conference on Database and expert systems applications: Part II, DEXA'10, pp.225-239, 2010. ,
Mining multi-dimensional constrained gradients in data cubes, Proceedings of the 27th International Conference on Very Large Data Bases, VLDB '01, pp.321-330, 2001. ,
Meta-rule-guided mining of association rules in relational databases, Proceedings of the 1st of the 1st International Workshop on Integration of Knowledge Discovery with Deductive and Object- Oriented Databases, pp.39-46, 1995. ,
Closed Sets for Labeled Data, Journal of Machine Learning Research, vol.9, pp.559-580, 2008. ,
DOI : 10.1007/11871637_19
Application-independent feature construction based on almost-closedness properties, Knowledge and Information Systems, vol.22, issue.3, pp.87-111, 2012. ,
DOI : 10.1007/s10115-010-0369-x
URL : https://hal.archives-ouvertes.fr/hal-01354374
Interestingness measures for data mining, ACM Computing Surveys, vol.38, issue.3, 2006. ,
DOI : 10.1145/1132960.1132963
Discovery and Application of Functional Dependencies in Conjunctive Query Mining, Proceedings of the 12th international conference on Data warehousing and knowledge discovery, pp.142-156, 2010. ,
DOI : 10.1007/978-3-642-15105-7_12
URL : https://hal.archives-ouvertes.fr/hal-00522913
Mining Association Rules of Simple Conjunctive Queries, Proceedings of the 8th SIAM International Conference on Data Mining, SDM '08, pp.96-107, 2008. ,
DOI : 10.1137/1.9781611972788.9
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
Efficiently using prefix-trees in mining frequent itemsets, Proceedings of the ICDM 2003 Workshop on Frequent Itemset Mining Implementations, volume 90 of FIMI '03. CEUR-WS.org, 2003. ,
Discovery of multiple-level association rules from large databases, Proceedings of the 21th International Conference on Very Large Data Bases, VLDB '95, pp.420-431, 1995. ,
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
Mining Frequent ??-Free Patterns in Large Databases, Discovery Science, pp.124-136, 2005. ,
DOI : 10.1007/11563983_12
Mining Frequent Disjunctive Selection Queries, Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II, DEXA '11, pp.90-96, 2011. ,
DOI : 10.1016/j.datak.2009.05.001
Cubegrades: Generalizing association rules, Data Mining and Knowledge Discovery, vol.6, issue.3, pp.219-257, 2002. ,
DOI : 10.1023/A:1015417610840
GTRACE2: Improving Performance Using Labeled Union Graphs, Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining, PAKDD '10, pp.178-188, 2010. ,
DOI : 10.1007/978-3-642-13672-6_18
FRISSMiner: Mining Frequent Graph Sequence Patterns Induced by Vertices, Proceedings of the 10th SIAM International Conference on Data Mining, SDM '10, pp.466-477, 2010. ,
DOI : 10.1587/transinf.E95.D.1590
TRIAS--An Algorithm for Mining Iceberg Tri-Lattices, Sixth International Conference on Data Mining (ICDM'06), pp.907-911, 2006. ,
DOI : 10.1109/ICDM.2006.162
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.83.489
Mining all frequent projection-selection queries from a relational table Advances in database technology Mining frequent conjunctive queries in star schemas, Proceedings of the 11th international conference on Extending database technology Proceedings of the 2009 International Database Engineering & Applications Symposium, IDEAS '09, pp.368-379, 2008. ,
A dichotomous algorithm for association rule mining, Proceedings of the 15th International Workshop on Database and Expert Systems Applications, DEXA '04, pp.567-571, 2004. ,
Optimisation de requêtes inductives: ApplicationáApplication´Applicationá l'extraction sous contraintes de r` egles d'association, 2002. ,
Mining frequent closed cubes in 3d datasets, Proceedings of the 32nd international conference on Very large data bases, VLDB '06, pp.811-822, 2006. ,
Metarule-guided mining of multi-dimensional association rules using data cubes, Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining, KDD '97, pp.207-210, 1997. ,
Concise Representations of Association Rules, Pattern Detection and Discovery, pp.92-109, 2002. ,
DOI : 10.1007/3-540-45728-3_8
Structure and evolution of online social networks, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp.611-617 ,
Mining Periodic Behavior in Dynamic Social Networks, 2008 Eighth IEEE International Conference on Data Mining, pp.373-382, 2008. ,
DOI : 10.1109/ICDM.2008.104
On selecting interestingness measures for association rules: User oriented description and multiple criteria decision aid, European Journal of Operational Research, vol.184, issue.2, pp.610-626, 2008. ,
DOI : 10.1016/j.ejor.2006.10.059
Graphs over time, Proceeding of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining , KDD '05, pp.177-187, 2005. ,
DOI : 10.1145/1081870.1081893
Pincer search: A new algorithm for discovering the maximum frequent set, Proceedings of the 6th International Conference on Extending Database Technology, EDBT '98, pp.105-119, 1998. ,
Mining optimal actions for profitable CRM, 2002 IEEE International Conference on Data Mining, 2002. Proceedings., pp.767-770, 2002. ,
DOI : 10.1109/ICDM.2002.1184049
Fast and memory efficient mining of frequent closed itemsets, IEEE Transactions on Knowledge and Data Engineering, vol.18, issue.1, pp.21-36, 2006. ,
DOI : 10.1109/TKDE.2006.10
Implications partielles dans un contexte, Mathématiques et Sciences Humaines, issue.113, pp.2935-55, 1991. ,
Multiple uses of frequent sets and condensed representations (extended abstract), Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, KDD '96, pp.189-194, 1996. ,
Levelwise search and borders of theories in knowledgediscovery, Data Mining and Knowledge Discovery, vol.1, issue.3, pp.241-258, 1997. ,
DOI : 10.1023/A:1009796218281
Enhanced mining of association rules from data cubes, Proceedings of the 9th ACM international workshop on Data warehousing and OLAP, DOLAP '06, pp.11-18, 2006. ,
Mining Triadic Association Rules from Ternary Relations, Proceedings of the 9th international conference on Formal concept analysis, pp.204-218, 2011. ,
DOI : 10.1007/BF01108624
Mining generalised disjunctive association rules, Proceedings of the 10th international conference on Information and knowledge management, CIKM '01, pp.482-489, 2001. ,
Exploratory mining and pruning optimizations of constrained associations rules, Proceedings of the 1998 ACM SIGMOD international conference on Management of data, SIGMOD '98, pp.13-24, 1998. ,
Adaptive and resource-aware mining of frequent sets, 2002 IEEE International Conference on Data Mining, 2002. Proceedings., pp.338-345, 2002. ,
DOI : 10.1109/ICDM.2002.1183921
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
Efficient mining of association rules using closed itemset lattices, Information Systems, vol.24, issue.1, pp.25-46, 1999. ,
DOI : 10.1016/S0306-4379(99)00003-4
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
Can we push more constraints into frequent pattern mining?, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '00, pp.350-354, 2000. ,
DOI : 10.1145/347090.347166
Closet: An efficient algorithm for mining frequent closed itemsets, In ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp.21-30, 2000. ,
Un cadre générique pour la co-classification sous contraintes : applicationàapplicationà l'analyse du transcriptome, 2006. ,
A theory of inductive query answering, Inductive Databases and Cosntraint-Based Data Mining, pp.79-103, 2010. ,
Using transposition for pattern discovery from microarray data, Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery , DMKD '03, pp.73-79, 2003. ,
DOI : 10.1145/882082.882099
URL : https://hal.archives-ouvertes.fr/hal-00324821
Constraint-Based Pattern Mining in Dynamic Graphs, 2009 Ninth IEEE International Conference on Data Mining, pp.950-955, 2009. ,
DOI : 10.1109/ICDM.2009.99
URL : https://hal.archives-ouvertes.fr/hal-01437815
Efficiently mining long patterns from databases, Proceedings of the 1998 ACM SIGMOD international conference on Management of data, pp.85-93, 1998. ,
Interestingness via what is not interesting, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '99, pp.332-336, 1999. ,
DOI : 10.1145/312129.312272
An efficient algorithm for mining association rules in large databases, Proceedings of the 21th International Conference on Very Large Data Bases, VLDB '95, pp.432-444, 1995. ,
Mining Association Rules in Folksonomies, Data Science and Classification, pp.261-270, 2006. ,
DOI : 10.1007/3-540-34416-0_28
On subjective measures of interestingness in knowledge discovery, Proceedings of the 1st International Conference on Knowledge Discovery and Data Mining, KDD '95, pp.275-281, 1995. ,
What makes patterns interesting in knowledge discovery systems, IEEE Transactions on Knowledge and Data Engineering, vol.8, issue.6, pp.970-974, 1996. ,
DOI : 10.1109/69.553165
Un cadre générique de découverte de motifs sous contraintes fondé sur des primitives, 2006. ,
An Efficient Framework for Mining Flexible Constraints, Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining, PAKDD'05, pp.661-671 ,
DOI : 10.1007/11430919_76
URL : https://hal.archives-ouvertes.fr/hal-00324837
Mining generalized association rules, Proceedings of the 21th International Conference on Very Large Data Bases, VLDB '95, pp.407-419, 1995. ,
DOI : 10.1016/S0167-739X(97)00019-8
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.40.7602
Intelligent Structuring and Reducing of Association Rules with Formal Concept Analysis, Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence, KI '01, pp.335-350, 2001. ,
DOI : 10.1007/3-540-45422-5_24
URL : https://hal.archives-ouvertes.fr/inria-00100665
Computing iceberg concept lattices with Titanic, Data & Knowledge Engineering, vol.42, issue.2, pp.189-222, 2002. ,
DOI : 10.1016/S0169-023X(02)00057-5
URL : https://hal.archives-ouvertes.fr/hal-00578830
GraphScope, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '07, pp.687-696, 2007. ,
DOI : 10.1145/1281192.1281266
Selecting the right interestingness measure for association patterns, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '02, pp.32-41, 2002. ,
DOI : 10.1145/775047.775053
Introduction to Data Mining, 2005. ,
Efficient single-pass frequent pattern mining using a prefix-tree, Information Sciences, vol.179, issue.5, pp.559-583, 2009. ,
DOI : 10.1016/j.ins.2008.10.027
Mining Association Rules in Data Warehouses, International Journal of Data Warehousing and Mining, vol.1, issue.3, pp.28-62, 2005. ,
DOI : 10.4018/jdwm.2005070103
Sampling large databases for association rules, Proceedings of the 22th International Conference on Very Large Data Bases, VLDB '96, pp.134-145, 1996. ,
Colibri, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 08, pp.686-694, 2008. ,
DOI : 10.1145/1401890.1401973
A Clustering of Interestingness Measures, Discovery Science, pp.290-297, 2004. ,
DOI : 10.1007/978-3-540-30214-8_23
CLOSET+, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '03, pp.236-245, 2003. ,
DOI : 10.1145/956750.956779
Concept lattices and conceptual knowledge systems, Computers & Mathematics with Applications, vol.23, issue.6-9, pp.493-515, 1992. ,
DOI : 10.1016/0898-1221(92)90120-7
A new approach to mine frequent patterns using item-transformation methods, Information Systems, vol.32, issue.7, pp.1056-1072, 2007. ,
DOI : 10.1016/j.is.2007.01.001
Learning patterns in the dynamics of biological networks, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pp.977-986, 2009. ,
Scalable algorithms for association mining, IEEE Trans. on Knowl. and Data Eng, vol.12, pp.372-390, 2000. ,
Mining non-redundant association rules, Data Min. Knowl. Discov, vol.9, issue.3, pp.223-248, 2004. ,
Fast vertical mining using diffsets, Proceedings of the 9th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '03, pp.326-335, 2003. ,
Charm: An efficient algorithm for closed itemset mining, Proceedings of the 2nd SIAM International Conference on Data Mining, SDM '02, pp.457-473, 2002. ,
Nous considérons qu'une association peut comporter de quelques sous-ensembles de certains domaines différents et que sa fréquence doitêtre doitêtre définie en terme des autre domains, i. e., les domaines que elle ne comporte pas. Par exemple, dans une relation 3-aire P roduits × Saisons × Clients, une association peutêtrepeutêtre un ensemble de produits, ou un ensemble de saisons, mais elle peut aussi concerneràconcernerà la fois des produits et des saisons, etc. Dans le contexte de la relation n-aires Comment pouvons-nous exprimer de telles associations? Comment pouvons-nous préciser l'intérêt subjectif de telles associations ,
?D ? = {D 1 ,
représentée Figure B.1, {p 1 , p 2 } × {s 1 } et {p 1 , p 2 } × {s 1 , s 2 } sont deux associations sur {D 1 , D 2 }. Par contre ,
Par exemple, dans R E , le domaine support d'une association sur {D 1 , D 2 } est D 3 . Le support d'une association est un sous-ensemble of le domaine support. La définition suivante utilise l'opérateur de concaténation noté ·. On a, par exemple ,
Support d'une association) ?D ? ? D, soit X une association sur D ? , son support noté s(X) est : s(X) ,
Considons des exemples de supports des trois associations dans R E . ? s({p 1 , p 2 } × {s 1 }) = {o 1 ,