R. Agrawal, T. Imieli?ski, and A. Swami, Mining Association Rules between Sets of Items in Large Databases, Proc. SIGMOD '93, pp.207-216, 1993.

R. Agrawal, C. John, and . Shafer, Parallel mining of association rules. Knowledge and Data Engineering, IEEE Transactions on, issue.6, pp.962-969, 1996.

R. Agrawal and R. Srikant, Fast algorithms for mining association rules in large databases, Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), pp.487-499, 1994.

C. Anderson, The Long Tail: Why the Future of Business Is Selling Less of More. Hyperion, 2006.

M. Armbrust, R. S. Xin, C. Lian, Y. Huai, D. Liu et al., Spark SQL, Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, SIGMOD '15, pp.1383-1394, 2015.
DOI : 10.1145/2723372.2742797

T. Calders and B. Goethals, Non-derivable itemset mining, Data Mining and Knowledge Discovery, vol.6, issue.5, pp.171-206, 2007.
DOI : 10.1007/s10618-006-0054-6

A. Ceglar and J. F. Roddick, Association mining, ACM Computing Surveys, vol.38, issue.2, 2006.
DOI : 10.1145/1132956.1132958

W. David, J. Cheung, . Han, T. Vincent, A. W. Ng et al., A fast distributed algorithm for mining association rules, Parallel and Distributed Information Systems Fourth International Conference on, pp.31-42, 1996.

J. Kun-ta-chuang, M. Huang, and . Chen, Mining top-k frequent patterns in the presence of the memory constraint, The VLDB Journal, vol.30, issue.2, pp.1321-1344, 2007.
DOI : 10.1007/s00778-007-0078-6

W. J. , C. , and K. E. Hillstrom, Chebyshev approximations for the natural logarithm of the gamma function, Mathematics of Computation, vol.21, issue.98, pp.198-203, 1967.

W. W. Daniel, Applied Nonparametric Statistics, Houghton Mifflin, 1978.

J. Dean and S. Ghemawat, MapReduce, Proceedings of the 6th Symposium on Operating System Design and Implementation (OSDI), 2004.
DOI : 10.1145/1327452.1327492

V. Dzyuba, M. Van-leeuwen, S. Nijssen, and L. De-raedt, Interactive Learning of Pattern Rankings, International Journal on Artificial Intelligence Tools, vol.23, issue.06, p.1460026, 2014.
DOI : 10.1142/S0218213014600264

R. Fagin, A. Lotem, and M. Naor, Optimal aggregation algorithms for middleware, Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems , PODS '01, pp.102-113, 2001.
DOI : 10.1145/375551.375567

P. Fournier-viger, A. Gomariz, T. Gueniche, A. Soltani, C. Wu et al., Spmf: a java open-source pattern mining library, The Journal of Machine Learning Research, vol.15, issue.1, pp.3389-3393, 2014.

P. Suganthan, G. C. , C. Sun, K. Gayatri, K. et al., Why Big Data Industrial Systems Need Rules and What We Can Do About It, Proc. SIGMOD '15, pp.265-276, 2015.

L. Geng and H. J. Hamilton, Interestingness measures for data mining, ACM Computing Surveys, vol.38, issue.3, 2006.
DOI : 10.1145/1132960.1132963

A. Ghoting, G. Buehrer, S. Parthasarathy, D. Kim, A. Nguyen et al., Cache-conscious frequent pattern mining on modern and emerging processors, The VLDB Journal, vol.16, issue.1, pp.77-96, 2007.
DOI : 10.1007/s00778-006-0025-y

S. Goel, A. Broder, E. Gabrilovich, and B. Pang, Anatomy of the long tail, Proceedings of the third ACM international conference on Web search and data mining, WSDM '10, pp.201-210, 2010.
DOI : 10.1145/1718487.1718513

B. Goethals, S. Nijssen, J. Mohammed, and . Zaki, Open source data mining, ACM SIGKDD Explorations Newsletter, vol.7, issue.2, pp.143-144, 2005.
DOI : 10.1145/1117454.1117476

J. Han, J. Pegoeli, Y. Yin, R. Mao, 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

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

J. Han, J. Wang, Y. Lu, and P. Tzvetkov, Mining top-k frequent closed patterns without minimum support, Proceedings of the International Conference on Data Mining (ICDM), pp.211-218, 2002.

T. Shin-ichi-minato, H. Uno, and . Arimura, Lcm over zbdds: Fast generation of very large-scale frequent itemsets using a compact graph-based representation, Advances in Knowledge Discovery and Data Mining, pp.234-246, 2008.

F. Ihab, G. Ilyas, M. A. Beskales, and . Soliman, A survey of top-k query processing techniques in relational database systems, ACM Computing Surveys, vol.4011, issue.4, pp.1-1158, 2008.

A. Jacobs, The pathologies of big data, Communications of the ACM, vol.52, issue.8, pp.36-44, 2009.
DOI : 10.1145/1536616.1536632

K. Järvelin and J. Kekäläinen, Cumulated gain-based evaluation of IR techniques, ACM Transactions on Information Systems, vol.20, issue.4, pp.422-446, 2002.
DOI : 10.1145/582415.582418

M. G. Kendall, A NEW MEASURE OF RANK CORRELATION, Biometrika, vol.30, issue.1-2, pp.81-93, 1938.
DOI : 10.1093/biomet/30.1-2.81

A. Kumar, M. Kantardzic, and S. Madden, Guest Editors' Introduction: Distributed Data Mining--Framework and Implementations, IEEE Internet Computing, vol.10, issue.4, p.15, 2006.
DOI : 10.1109/MIC.2006.79

T. Le and D. Lo, Beyond support and confidence: Exploring interestingness measures for rule-based specification mining, 2015 IEEE 22nd International Conference on Software Analysis, Evolution, and Reengineering (SANER), pp.331-340, 2015.
DOI : 10.1109/SANER.2015.7081843

Y. Le-bras, P. Lenca, and S. Lallich, Mining interesting rules without support requirement: a general universal existential upward closure property, volume 8 of Annals of information systems, chapter Data Mining, pp.75-98, 2010.

J. Lee, Y. Sik-kwon, F. Farber, M. Muehle, C. Lee et al., Sap hana distributed in-memory database system: Transaction, session, and metadata management, Data Engineering (ICDE), 2013 IEEE 29th International Conference on, pp.1165-1173, 2013.

P. Lenca, B. Vaillant, P. Meyer, and S. Lallich, Association Rule Interestingness Measures: Experimental and Theoretical Studies, Quality Measures in Data Mining, pp.51-76, 2007.
DOI : 10.1007/978-3-540-44918-8_3
URL : https://hal.archives-ouvertes.fr/hal-01373475

H. Li, Y. Wang, D. Zhang, M. Zhang, and E. Y. Chang, Pfp, Proceedings of the 2008 ACM conference on Recommender systems, RecSys '08, pp.107-114, 2008.
DOI : 10.1145/1454008.1454027

M. Lin, P. Lee, and S. Hsueh, Apriori-based frequent itemset mining algorithms on MapReduce, Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication, ICUIMC '12, p.76, 2012.
DOI : 10.1145/2184751.2184842

J. Lindström, V. Raatikka, J. Ruuth, P. Soini, and K. Vakkila, Ibm soliddb: In-memory database optimized for extreme speed and availability, IEEE Data Engineering Bulletin, vol.36, issue.2, pp.14-20, 2013.

G. Liu, M. Feng, Y. Wang, L. Wong, S. Ng et al., Towards exploratory hypothesis testing and analysis, 2011 IEEE 27th International Conference on Data Engineering, pp.745-756, 2011.
DOI : 10.1109/ICDE.2011.5767907

G. Liu, A. Suchitra, and L. Wong, A performance study of three disk-based structures for indexing and querying frequent itemsets, Proceedings of the VLDB Endowment, vol.6, issue.7, pp.505-516, 2013.
DOI : 10.14778/2536349.2536351

C. Lucchese, S. Orlando, and R. Perego, Parallel Mining of Frequent Closed Patterns: Harnessing Modern Computer Architectures, Seventh IEEE International Conference on Data Mining (ICDM 2007), pp.242-251, 2007.
DOI : 10.1109/ICDM.2007.13

C. Lucchese, S. Orlando, R. Perego, and F. Silvestri, Webdocs: a real-life huge transactional dataset, Proceedings of the Workshop on Frequent Itemset Mining Implementations (FIMI), 2004.

M. Mampaey, N. Tatti, and J. Vreeken, Tell me what i need to know, Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '11, pp.573-581, 2011.
DOI : 10.1145/2020408.2020499

M. Mampaey, J. Vreeken, and N. Tatti, Summarizing data succinctly with the most informative itemsets, ACM Transactions on Knowledge Discovery from Data, vol.6, issue.4, pp.1-1642, 2012.
DOI : 10.1145/2382577.2382580

L. Mary and . Mchugh, The odds ratio: calculation, usage, and interpretation, Biochemia Medica, vol.19, issue.2, pp.120-126, 2009.

S. Minato, T. Uno, K. Tsuda, A. Terada, and J. Sese, A Fast Method of Statistical Assessment for Combinatorial Hypotheses Based on Frequent Itemset Enumeration, Machine Learning and Knowledge Discovery in Databases, pp.422-436, 2014.
DOI : 10.1007/978-3-662-44851-9_27

S. Moens, E. Aksehirli, and B. Goethals, Frequent Itemset Mining for Big Data, 2013 IEEE International Conference on Big Data, 2013.
DOI : 10.1109/BigData.2013.6691742

T. Raymond, . Ng, V. Laks, J. Lakshmanan, A. Han et al., Exploratory mining and pruning optimizations of constrained associations rules, In ACM SIGMOD Record, vol.27, pp.13-24, 1998.

B. Négrevergne, A. Termier, J. Méhaut, and T. Uno, Discovering closed frequent itemsets on multicore: Parallelizing computations and optimizing memory accesses, 2010 International Conference on High Performance Computing & Simulation, pp.521-528, 2010.
DOI : 10.1109/HPCS.2010.5547082

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), pp.398-416, 1999.
DOI : 10.1007/3-540-49257-7_25
URL : https://hal.archives-ouvertes.fr/hal-00467747

J. Pei, J. Han, and R. Mao, Closet: An Efficient Algorithm for Mining Frequent Closed Itemsets, Proc. SIGMOD '00, pp.21-30, 2000.

G. Piatetsky-shapiro, Discovery, analysis, and presentation of strong rules. Knowledge discovery in databases, pp.229-238, 1991.

B. Rácz, F. Bodon, and L. Schmidt-thieme, On benchmarking frequent itemset mining algorithms, Proceedings of the 1st international workshop on open source data mining frequent pattern mining implementations, OSDM '05, pp.36-45, 2005.
DOI : 10.1145/1133905.1133911

M. Riondato, J. A. Debrabant, R. Fonseca, and E. Upfal, PARMA, Proceedings of the 21st ACM international conference on Information and knowledge management, CIKM '12, pp.85-94, 2012.
DOI : 10.1145/2396761.2396776

R. R. Sokal and C. D. Michener, A Statistical Method for Evaluating Systematic Relationships, Univ. Kans. Sci. Bull, vol.38, pp.1409-1438, 1958.

P. Tan, M. Steinbach, and V. Kumar, Introduction to Data Mining, 2007.

T. Uno, T. Asai, Y. Uchida, and H. Arimura, Lcm: An efficient algorithm for enumerating frequent closed item sets, FIMI, 2003.

T. Uno, T. Asai, Y. Uchida, and H. Arimura, An Efficient Algorithm for Enumerating Closed Patterns in Transaction Databases, Discovery Science, pp.16-31, 2004.
DOI : 10.1007/978-3-540-30214-8_2

T. Uno, M. Kiyomi, and H. Arimura, Lcm ver. 2: Efficient mining algorithms for frequent/closed/maximal itemsets, Proceedings of the Workshop on Frequent Itemset Mining Implementations (FIMI), 2004.

T. Uno, M. Kiyomi, and H. Arimura, LCM ver.3, Proceedings of the 1st international workshop on open source data mining frequent pattern mining implementations, OSDM '05, pp.77-86, 2005.
DOI : 10.1145/1133905.1133916

B. Vaillant, P. Lenca, and S. Lallich, A Clustering of Interestingness Measures, Discovery Science, pp.290-297, 2004.
DOI : 10.1007/978-3-540-30214-8_23

V. Kumar-vavilapalli, A. C. Murthy, C. Douglas, S. Agarwal, M. Konar et al., Apache Hadoop YARN, Proceedings of the 4th annual Symposium on Cloud Computing, SOCC '13, pp.1-516, 2013.
DOI : 10.1145/2523616.2523633

J. Vreeken, M. Van-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

J. Wang, J. Han, Y. Lu, and P. Tzvetkov, Tfp: An efficient algorithm for mining top-k frequent closed itemsets. Knowledge and Data Engineering, IEEE Transactions on, vol.17, issue.5, pp.652-663, 2005.

J. Wang, J. Han, and J. Pei, 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

M. Zaharia, M. Chowdhury, J. Michael, S. Franklin, I. Shenker et al., Spark: Cluster computing with working sets. HotCloud, pp.10-10, 2010.

R. Osmar, M. Zaïane, P. El-hajj, and . Lu, Fast parallel association rule mining without candidacy generation, Data Mining Proceedings IEEE International Conference on, pp.665-668, 2001.

J. Mohammed and . Zaki, Scalable algorithms for association mining Knowledge and Data Engineering, IEEE Transactions on, vol.12, issue.3, pp.372-390, 2000.

J. Mohammed, C. Zaki, and . Hsiao, Charm: An efficient algorithm for closed itemset mining, SDM, pp.457-473, 2002.

J. Mohammed, C. Zaki, and . Hsiao, Charm: An efficient algorithm for closed association rule mining, 1999.

J. Mohammed, S. Zaki, M. Parthasarathy, W. Ogihara, and . Li, Parallel algorithms for discovery of association rules, Data mining and knowledge discovery, vol.1, issue.4, pp.343-373, 1997.

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

T. Terje-inspector and . Norse, Siriusmo -Einmal In Der Woche Schreien 6. Sebastien Leger -Snow Flakes 7. Noir Désir -The Holy Economic War 8. NoFX -Freedom Lika Shopping Cart 9

A. Town, Easy Star All-Stars feat. Sugar Minott -When I'm Sixty-Four

L. Feat, Tigran Hamasyan -Ruiselede 17. Stupeflip -Stupeflip Vite ! 18. Guts -As The World Turns 19