R. Agrawal, T. Imielinski, and A. Swami, Mining Association Rules between Sets of Items in Large Database, pp.207-216, 1993.

Y. Asses, A. Buzmakov, T. Bourquard, S. O. Kuznetsov, and A. Napoli, A hybrid classification approach based on fca and emerging patterns-an application for the classification of biological inhibitors, Int. Conf. on Concept Lattices and Their Applications, pp.211-222, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00761586

J. Bao, Y. Zheng, D. Wilkie, and M. Mokbel, Recommendations in location-based social networks: a survey, GeoInformatica, vol.19, issue.3, pp.525-565, 2015.

M. Benkert, J. Gudmundsson, F. Hübner, and T. Wolle, Reporting flock patterns, Computational Geometry, vol.41, issue.3, pp.111-125, 2008.

A. Buzmakov, E. Egho, N. Jay, S. O. Kuznetsov, A. Napoli et al., Fca and pattern structures for mining care trajectories, In: Int. Workshop on What Can FCA Do for Artificial Intelligence, pp.7-14, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00910290

E. Cesario, C. Comito, and D. Talia, Trajectory Data Analysis Over a Cloud-Based Framework for Smart City Analytics, pp.143-162, 2014.

L. Chen, L. Liu, B. Chen, and H. Jia, Research on patterns mining method for moving objects, Advanced Science and Technology Letters, vol.123, pp.200-205, 2016.

G. Dong and J. Li, Efficient Mining of Emerging Patterns: Discovering Trends and Differences, pp.43-52, 1999.
DOI : 10.1145/312129.312191

N. Durand and M. Quafafou, Approximation of Frequent Itemset Border by Computing Approximate Minimal Hypergraph Transversals, Int. Conf. on Data Warehousing and Knowledge Discovery, pp.357-368, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01465114

N. Durand and M. Quafafou, Frequent Itemset Border Approximation by Dualization, Transactions on Large-Scale Data-and Knowledge-Centered Systems (TLDKS), vol.26, pp.32-60, 2016.
DOI : 10.1007/978-3-662-49784-5_2

URL : https://hal.archives-ouvertes.fr/hal-01465110

N. Durand and A. Soulet, Emerging Overlapping Clusters for Characterizing the Stage of Liver Fibrosis, ECML/PKDD'05 Discovery Challenge on Hepatitis Data, pp.139-150, 2005.
URL : https://hal.archives-ouvertes.fr/hal-00324792

B. Ganter and R. Wille, Formal concept analysis: mathematical foundations, 2012.

X. Geng, T. Uno, and H. Arimura, Trajectory pattern mining in practice-algorithms for mining flock patterns from trajectories, Int. Conf. on Knowledge Discovery and Information Retrieval and the Int. Conf. on Knowledge Management and Information Sharing, pp.143-151, 2013.

F. Giannotti, M. Nanni, F. Pinelli, and D. Pedreschi, Trajectory pattern mining, ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp.330-339, 2007.

Y. Hu, Y. Yang, and B. Huang, A comprehensive survey of recommendation system based on taxi gps trajectory, Int. Conf. on Service Science, pp.99-105, 2015.
DOI : 10.1109/icss.2015.31

N. Jay, G. Nuemi, M. Gadreau, and C. Quantin, A data mining approach for grouping and analyzing trajectories of care using claim data: the example of breast cancer, BMC Medical Informatics and decision making, vol.13, issue.1, p.130, 2013.
URL : https://hal.archives-ouvertes.fr/inserm-00917359

A. J. Lee, Y. A. Chen, and W. C. Ip, Mining frequent trajectory patterns in spatial-temporal databases, Information Sciences, vol.179, issue.13, pp.2218-2231, 2009.
DOI : 10.1016/j.ins.2009.02.016

J. G. Lee, J. Han, and X. Li, A unifying framework of mining trajectory patterns of various temporal tightness, IEEE Trans. on Knowl. and Data Eng, vol.27, issue.6, pp.1478-1490, 2015.

J. G. Lee, J. Han, X. Li, and H. Gonzalez, Traclass: Trajectory classification using hierarchical region-based and trajectory-based clustering, Proc. VLDB Endowment, vol.1, issue.1, pp.1081-1094, 2008.

J. G. Lee, J. Han, and K. Y. Whang, Trajectory clustering: A partition-and-group framework, ACM SIGMOD Int. Conf. on Management of Data, pp.593-604, 2007.
DOI : 10.1145/1247480.1247546

J. Li, G. Dong, and K. Ramamohanarao, Instance-based classification by emerging patterns, European Conf. on Principles of Data Mining and Knowledge Discovery, pp.191-200, 2000.

J. Li, M. Yang, N. Liu, Z. Wang, and L. Yu, A trajectory data clustering method based on dynamic grid density, Int. Journal of Grid and Distributed Computing, vol.8, issue.2, pp.1-8, 2015.
DOI : 10.14257/ijgdc.2015.8.2.01

URL : https://doi.org/10.14257/ijgdc.2015.8.2.01

Z. Li, Spatiotemporal Pattern Mining: Algorithms and Applications, pp.283-306, 2014.
DOI : 10.1007/978-3-319-07821-2_12

Z. Li, M. Ji, J. G. Lee, L. A. Tang, Y. Yu et al., Movemine: Mining moving object databases, ACM SIGMOD Int. Conf. on Management of data, pp.1203-1206, 2010.

M. Lin and W. J. Hsu, Mining GPS data for mobility patterns: A survey, Pervasive and Mobile Computing, vol.12, pp.1-16, 2014.
DOI : 10.1016/j.pmcj.2013.06.005

H. Naim, M. Aznag, N. Durand, and M. Quafafou, Semantic Pattern Mining Based Web Service Recommendation, IEEE Int. Conf. on Service Oriented Computing, pp.417-432, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01465113

H. Naim, M. Aznag, M. Quafafou, and N. Durand, Probabilistic Approach for Diversifying Web Services Discovery and Composition, IEEE Int. Conf. on Web Services, pp.73-80, 2016.
DOI : 10.1109/icws.2016.19

URL : https://hal.archives-ouvertes.fr/hal-01465112

G. Pan, G. Qi, W. Zhang, S. Li, Z. Wu et al., Trace Analysis and Mining for Smart Cities: Issues, Methods, and Applications, IEEE Communications Magazine, vol.51, issue.6, pp.120-126, 2013.

C. Parent, S. Spaccapietra, C. Renso, G. Andrienko, N. Andrienko et al., Semantic trajectories modeling and analysis, ACM Computing Surveys (CSUR), vol.45, issue.4, pp.1-32, 2013.
DOI : 10.1145/2501654.2501656

URL : http://www.uhasselt.be/Documents/datasim/Papers/Semantic-Trajectories-Modeling-and-Analysis.pdf

N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, 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

J. Poelmans, D. I. Ignatov, S. O. Kuznetsov, and G. Dedene, Formal concept analysis in knowledge processing: A survey on applications, Expert Systems with Applications, vol.40, issue.16, pp.6538-6560, 2013.
DOI : 10.1016/j.eswa.2013.05.009

A. Soulet, B. Crémilleux, and F. Rioult, Condensed Representation of Emerging Patterns, Pacific-Asia Conf. on Knowledge Discovery and Data Mining, p.127132, 2004.
DOI : 10.1007/978-3-540-24775-3_16

URL : https://hal.archives-ouvertes.fr/hal-00324836

A. Y. Xue, J. Qi, X. Xie, R. Zhang, J. Huang et al., Solving the data sparsity problem in destination prediction, Int. Journal on Very Large Data Bases, vol.24, issue.2, pp.219-243, 2015.
DOI : 10.1007/s00778-014-0369-7

N. J. Yuan, Y. Zheng, X. Xie, and G. Sun, Driving with knowledge from the physical world, ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp.316-324, 2011.
DOI : 10.1145/2020408.2020462

N. J. Yuan, Y. Zheng, X. Xie, Y. Wang, K. Zheng et al., Discovering Urban Functional Zones Using Latent Activity Trajectories, IEEE Trans. on Knowl. and Data Eng, vol.27, issue.3, pp.712-725, 2015.
DOI : 10.1109/tkde.2014.2345405

N. J. Yuan, Y. Zheng, C. Zhang, W. Xie, X. Xie et al., T-drive: Driving directions based on taxi trajectories, Int. Conf. on Advances in Geographic Information Systems, pp.99-108, 2010.

M. J. Zaki and C. J. Hsiao, Efficient algorithms for mining closed itemsets and their lattice structure, IEEE Trans. on Knowl. and Data Eng, vol.17, issue.4, pp.462-478, 2005.
DOI : 10.1109/tkde.2005.60

L. Zhang, L. Liu, Z. Xia, W. Li, and Q. Fan, Sparse trajectory prediction based on multiple entropy measures, Entropy, vol.18, issue.9, pp.1-14, 2016.
DOI : 10.3390/e18090327

URL : https://www.mdpi.com/1099-4300/18/9/327/pdf

Y. Zheng, Trajectory Data Mining: An Overview, ACM Trans. Intell. Syst. Technol, vol.6, issue.3, pp.29-41, 2015.
DOI : 10.1145/2743025

Y. Zheng and X. Zhou, Computing with spatial trajectories, 2011.