T. Multi_move, D. Nhat-hai-phan, P. Ienco, M. Poncelet, and . Teisseire, Mining Representative Movement Patterns through Compression, The 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining Goal Coast, 2013.

D. Nhat-hai-phan, P. Ienco, M. Poncelet, and . Teisseire, Mining Fuzzy Moving Object Clusters, Proceedings of the 8th International Conference on Advanced Data Mining and Applications (ADMA), 2012.

D. Nhat-hai-phan, P. Ienco, M. Poncelet, and . Teisseire, Mining Time Relaxed Gradual Moving Object Clusters, Proceedings of the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS 2012), 2012.

F. Bouillot, N. Hai-phan, N. Béchet, S. Bringay, D. Ienco et al., How to Extract Relevant Knowledge from Tweets?, The 7th International Workshop on Information Search, Integration and Personalization (ISIP'2012), 2012.
DOI : 10.1007/978-3-642-40140-4_12
URL : https://hal.archives-ouvertes.fr/lirmm-00798662

P. Nhat-hai-phan, M. Poncelet, and . Teisseire, GeT_Move: An Efficient and Unifying Spatio-temporal Pattern Mining Algorithm for Moving Objects, Proceedings of the 11th International Symposium on Intelligent Data Analysis, 2012.
DOI : 10.1007/978-3-642-34156-4_26

P. Nhat-hai-phan, M. Poncelet, and . Teisseire, An Efficient Spatio- Temporal Mining Approach to Really Know Who Travels with Whom!, 28th Advance in Data Mining, p.163, 2012.

D. Nhat-hai-phan, P. Ienco, M. Poncelet, and . Teisseire, Extracting Trajectories through an Efficient and Unifying Spatio-Temporal Patten Mining System, Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2012), Demo Paper, 2012.

P. Nhat-hai-phan, M. Poncelet, and . Teisseire, Moving Objects: Combining Gradual Rules and Spatio-Temporal Patterns, 2011 International Conference on Spatial Data Mining and Geographical Knowledge Services, 2011.

P. Nhat-hai-phan, M. Poncelet, and . Teisseire, An Efficient Spatio- Temporal Mining Approach to Really Know Who Travels with Whom!". Ingénierie des Systèmes d'Information (ISI special issue, selected papers from BDA'12), 2013.

R. Agrawal, R. Srikant-aung, K. L. Tan, R. Cilibrasi, and P. M. Vitányi, Fast algorithms for mining association rules in large databases Cited pages 11, 20, and 71 Discovery of evolving convoys Clustering by compression, VLDB '94 SSDBM, pp.487-499, 1994.

M. Ester, H. P. Kriegel, J. Sander, X. Xu, A. Gionis et al., A density-based algorithm for discovering clusters in large spatial databases with noise Assessing data mining results via swap randomization The minimum description length principle Computing longest duration flocks in trajectory data Extracting trajectories through an efficient and unifying spatio-temporal pattern mining system, ACM GIS ECML/PKDD, and 96. [9] P. N. Hai, D. Ienco, P. Poncelet, and M. Teisseire. Mining fuzzy moving object clusters. In ADMA, pp.226-231, 1996.

P. N. Hai, D. Ienco, P. Poncelet, and M. Teisseire, Mining time relaxed gradual moving object clusters, Proceedings of the 20th International Conference on Advances in Geographic Information Systems, SIGSPATIAL '12, pp.478-481, 2012.
DOI : 10.1145/2424321.2424394
URL : https://hal.archives-ouvertes.fr/lirmm-00798076

P. N. Hai, D. Ienco, P. Poncelet, and M. Teisseire, Mining Representative Movement Patterns through Compression, PAKDD, 2013
DOI : 10.1007/978-3-642-37453-1_26
URL : https://hal.archives-ouvertes.fr/lirmm-00798072

P. N. Hai, P. Poncelet, and M. Teisseire, Get_move: An efficient and unifying spatiotemporal pattern mining algorithm for moving objects, IDA, pp.276-288, 2012.
URL : https://hal.archives-ouvertes.fr/lirmm-00732660

P. N. Hai, P. Poncelet, and M. Teisseire, An efficient spatio-temporal mining approach to really know who travels with whom! In BDA 2012, pp.2012-56

J. Han, Z. Li, and L. A. Tang, Mining Moving Object, Trajectory and Traffic Data, Database Systems for Advanced Applications, pp.485-486, 2010.
DOI : 10.1007/978-3-642-12098-5_56

J. Han, J. Pei, and Y. Yin, Mining frequent patterns without candidate generation, ACM SIGMOD Record, vol.29, issue.2, pp.1-12, 2000.
DOI : 10.1145/335191.335372

Y. Huang, S. Shekhar, and H. Xiong, Discovering colocation patterns from spatial data sets: a general approach, IEEE Transactions on Knowledge and Data Engineering, vol.16, issue.12, pp.1472-1485, 2004.
DOI : 10.1109/TKDE.2004.90

C. S. Jensen, L. Dan, and B. C. Ooi, Continuous Clustering of Moving Objects, IEEE Transactions on Knowledge and Data Engineering, vol.19, issue.9, pp.1161-1174, 2007.
DOI : 10.1109/TKDE.2007.1054

H. Jeung, M. L. Yiu, X. Zhou, C. S. Jensen, and H. T. Shen, Discovery of convoys in trajectory databases, Proceedings of the VLDB Endowment, vol.1, issue.1, pp.1068-1080, 2008.
DOI : 10.14778/1453856.1453971

P. Kalnis, N. Mamoulis, and S. Bakiras, On discovering moving clusters in spatiotemporal data, SSTD '05, pp.364-381, 2005.

E. J. Keogh, S. Lonardi, C. A. Ratanamahatana, L. Wei, S. H. Lee et al., Compression-based data mining of sequential data, Data Mining and Knowledge Discovery, vol.11, issue.(2, pp.99-129, 2007.
DOI : 10.1007/s10618-006-0049-3

H. T. Lam, F. Moerchen, D. Fradkin, and T. Calders, Mining compressing sequential patterns, SDM, pp.319-330, 2012.

J. G. Lee, J. Han, and K. Y. Whang, Trajectory clustering, Proceedings of the 2007 ACM SIGMOD international conference on Management of data , SIGMOD '07, pp.593-604, 2007.
DOI : 10.1145/1247480.1247546

Z. Li, B. Ding, J. Han, and R. Kays, Swarm, Proc. VLDB Endow, pp.723-734, 2010.
DOI : 10.14778/1920841.1920934
URL : https://hal.archives-ouvertes.fr/hal-01504869

N. Mamoulis, H. Cao, G. Kollios, M. Hadjieleftheriou, Y. Tao et al., Mining, indexing, and querying historical spatio-temporal data, KDD '04, pp.236-245, 2004.
DOI : 10.1145/1014052.1014080

R. Milo, S. Shen-orr, S. Itzkovits, N. Kashtan, D. Chklovskii et al., Network Motifs: Simple Building Blocks of Complex Networks, Science, vol.298, issue.5594, p.298, 2002.
DOI : 10.1126/science.298.5594.824

R. Motwani and P. Raghavan, Randomized algorithms, 1995.

K. Smets and J. Vreeken, : Directly Mining Descriptive Patterns, SDM, pp.87-88, 2012.
DOI : 10.1137/1.9781611972825.21

L. A. Tang, Y. Zheng, J. Yuan, J. Han, A. Leung et al., On Discovery of Traveling Companions from Streaming Trajectories, 2012 IEEE 28th International Conference on Data Engineering, pp.186-197, 2012.
DOI : 10.1109/ICDE.2012.33

F. Verhein, Mining Complex Spatio-Temporal Sequence Patterns, SDM '09, pp.605-616, 2009.
DOI : 10.1137/1.9781611972795.52

M. R. Vieira, P. Bakalov, and V. J. Tsotras, On-line discovery of flock patterns in spatiotemporal data, ACM SIGSPATIAL GIS, pp.286-295, 2009.

J. Vreeken, M. Leeuwen, and A. Siebes, Krimp: mining itemsets that compress, Data Mining and Knowledge Discovery, vol.177, issue.1, pp.169-214, 2011.
DOI : 10.1007/s10618-010-0202-x

Y. Wang, E. P. Lim, and S. Y. Hwang, Efficient mining of group patterns from user movement data, Data & Knowledge Engineering, vol.57, issue.3, pp.240-282, 2006.
DOI : 10.1016/j.datak.2005.04.006

Z. Li, M. Ji, J. Lee, L. A. Tang, Y. Yu et al., MoveMine, Proceedings of the 2010 international conference on Management of data, SIGMOD '10, pp.1203-1206, 2010.
DOI : 10.1145/1807167.1807319

A. Knobbe and E. Ho, Pattern Teams, PKDD '06, pp.577-584, 2006.
DOI : 10.1007/11871637_58

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

A. Siebes and R. Kersten, A Structure Function for Transaction Data, SDM '11, pp.558-569, 2011.
DOI : 10.1137/1.9781611972818.48

H. Mannila and E. Terzi, Nestedness and segmented nestedness, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '07, pp.480-489, 2007.
DOI : 10.1145/1281192.1281245

T. Uno, M. Kiyomi, and H. Arimura, LCM ver. 2: Efficient mining algorithms for frequent/closed/maximal itemsets, Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations CEUR Workshop Proceedings, 2004.

A. O. Romero, Mining moving flock patterns in large spatio-temporal datasets using a frequent pattern mining approach, 2011.

A. Appice, M. Ceci, and D. Malerba, Mining Model Trees: A Multi-relational Approach, Proc. of the 13th International Conference on Inductive Logic Programming , ILP 2003, volume 2835 of LNAI, pp.4-21, 2003.
DOI : 10.1007/978-3-540-39917-9_3

H. Mannila, B. Goethals, and W. L. Page, Mining association rules of simple conjunctive queries, SDM 2008, pp.96-107, 2008.

T. Calders, B. Goethals, and S. Jaroszewicz, Mining rank-correlated sets of numerical attributes, Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '06, pp.96-105
DOI : 10.1145/1150402.1150417

L. Dehaspe and H. Toivonen, Discovery of frequent datalog patterns, Data Mining and Knowledge Discovery, vol.3, issue.1, pp.7-36, 1999.
DOI : 10.1023/A:1009863704807

L. Di-jorio, A. Laurent, and M. Teisseire, Mining Frequent Gradual Itemsets from Large Databases, pp.297-308, 2009.
DOI : 10.1007/3-540-44794-6_20

L. D?eroski and N. Lavra?, Relational data mining, 2001.

P. A. Flach and N. Lachiche, Naive Bayesian Classification of Structured Data, Machine Learning, pp.233-269, 2004.
DOI : 10.1023/B:MACH.0000039778.69032.ab

N. Friedman, L. Getoor, D. Koller, and A. Pfeffer, Learning probabilistic relational models, IJCAI, pp.1300-1309, 1999.

B. Goethals, W. L. Page, and M. Mampaey, Mining interesting sets and rules in relational databases, Proceedings of the 2010 ACM Symposium on Applied Computing, SAC '10, pp.997-1001, 2010.
DOI : 10.1145/1774088.1774299

E. Hullermeier, Association Rules for Expressing Gradual Dependencies, Principles of Data Mining and Knowledge Discovery, pp.200-211, 2002.
DOI : 10.1007/3-540-45681-3_17

A. Koopman and A. Siebes, Characteristic relational patterns, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pp.437-446, 2009.
DOI : 10.1145/1557019.1557071

A. Koopman and A. Siebes, Discovering Relational Item Sets Efficiently, SDM 2008, pp.108-119, 2008.
DOI : 10.1137/1.9781611972788.10

E. K. Ng, A. W. Fu, and K. Wang, Mining association rules from stars, ICDM 2002, pp.322-329, 2002.

S. Nijssen and J. Kok, Efficient Frequent Query Discovery in Farmer, PKDD 2003, pp.350-362, 2003.
DOI : 10.1007/978-3-540-39804-2_32

W. L. Page, Mining patterns in relational databases, p.119, 2009.

E. Spyropoulou and T. D. Bie, Interesting Multi-relational Patterns, 2011 IEEE 11th International Conference on Data Mining, pp.675-684, 2011.
DOI : 10.1109/ICDM.2011.82
URL : http://arxiv.org/abs/1106.4475

F. Zelezný and N. Lavrac, Propositionalization-based relational subgroup discovery with rsd, Machine Learning, pp.33-63, 2006.

S. Ayouni, A. Laurent, S. B. Yahia, and P. Poncelet, Mining Closed Gradual Patterns, pp.267-274, 2010.
DOI : 10.1007/978-3-642-13208-7_34
URL : https://hal.archives-ouvertes.fr/lirmm-00504029

M. J. Zaki, Mining Non-Redundant Association Rules, Data Mining and Knowledge Discovery, vol.9, issue.3, pp.223-248, 2004.
DOI : 10.1023/B:DAMI.0000040429.96086.c7

S. Rinzivillo, D. Pedreschi, M. Nanni, F. Giannotti, N. Andrienko et al., Visually driven analysis of movement data by progressive clustering, Information Visualization, vol.9, issue.3-4, pp.225-239, 2008.
DOI : 10.1057/PALGRAVE.IVS.9500183

G. L. Andrienko and N. V. Andrienko, Spatio-temporal aggregation for visual analysis of movements, 2008 IEEE Symposium on Visual Analytics Science and Technology, pp.51-58, 2008.
DOI : 10.1109/VAST.2008.4677356

G. L. Andrienko and N. V. Andrienko, Interactive cluster analysis of diverse types of spatiotemporal data, ACM SIGKDD Explorations Newsletter, vol.11, issue.2, pp.19-28, 2009.
DOI : 10.1145/1809400.1809405

G. L. Andrienko, N. V. Andrienko, S. Rinzivillo, M. Nanni, D. Pedreschi et al., Interactive visual clustering of large collections of trajectories, 2009 IEEE Symposium on Visual Analytics Science and Technology, pp.3-10, 2009.
DOI : 10.1109/VAST.2009.5332584

A. T. Palma, V. Bogorny, B. Kuijpers, and L. O. Alvares, A clustering-based approach for discovering interesting places in trajectories, Proceedings of the 2008 ACM symposium on Applied computing , SAC '08, pp.863-868, 2008.
DOI : 10.1145/1363686.1363886

J. H. Kang, W. Welbourne, B. Stewart, and G. Borriello, Extracting places from traces of locations, Proceedings of the 2nd ACM international workshop on Wireless mobile applications and services on WLAN hotspots, pp.110-118, 2004.

F. Giannotti, M. Nanni, F. Pinelli, and D. Pedreschi, Trajectory pattern mining, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '07, pp.330-339, 2007.
DOI : 10.1145/1281192.1281230

F. Bouillot, P. Poncelet, M. Roche, D. Ienco, E. Bigdeli et al., French presidential elections, Proceedings of the first edition workshop on Politics, elections and data, PLEAD '12, 2012.
DOI : 10.1145/2389661.2389669
URL : https://hal.archives-ouvertes.fr/lirmm-00801028