. , Two Level Decision Making (TLDM)

. .. Experiments,

. .. Conclusion, , p.141

. .. Contributions, 141 7.1.2 Fast Parallel Mining of Maximally Informative K-itemset from Data Stream

, Fast Parallel Ensemble of Ensembles of Classifiers, p.142

. .. Directions-for-future-work, , p.100

, John graunt, pp.2016-2025

. Thomas, , pp.2017-2019

, Rakesh agrawal, pp.2016-2025

L. Feng, Y. K. Chiam, and S. K. Lo, Text-mining techniques and tools for systematic literature reviews: A systematic literature review, 24th Asia-Pacific Software Engineering Conference, pp.41-50, 2017.

R. Agrawal and R. Srikant, Fast algorithms for mining association rules in large databases, Proceedings of 20th International Conference on Very Large Data Bases, VLDB'94, pp.487-499, 1994.

R. Wille, Restructuring lattice theory: an approach based on hierarchies of concepts, pp.445-470, 1982.

N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, Discovering frequent closed itemsets for association rules, Database Theory-ICDT '99, 7th International Conference, pp.398-416, 1999.
URL : https://hal.archives-ouvertes.fr/hal-00467747

S. Li, L. Li, and C. Han, Mining closed frequent itemset based on fp-tree, The 2009 IEEE International Conference on Granular Computing, pp.354-357, 2009.

J. Dean and S. Ghemawat, Mapreduce: simplified data processing on large clusters, Journal of Commun. ACM, pp.107-113, 2008.

M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, Spark: Cluster computing with working sets, 2010.

J. Han, J. Pei, and Y. Yin, Mining frequent patterns without candidate generation, Proceedings of the 6th International Conference on Management of Data, SIGMOD'00, pp.1-12, 2000.

H. Li, Y. Wang, D. Zhang, M. Zhang, and E. Y. Chang, Pfp: parallel fp-growth for query recommendation, Proceedings of the 2nd International Conference on Recommender Systems, pp.107-114, 2008.

A. J. Knobbe and E. K. Ho, Maximally informative k-itemsets and their efficient discovery, Proceedings of the 12th International Conference on Knowledge Discovery and Data Mining, ACMSIGKDD'06, pp.237-244, 2006.

N. V. Sawant, K. Shah, and V. A. Bharadi, Survey on data mining classification techniques, Proceedings of the International Conference & Workshop on Emerging Trends in Technology, pp.1380-1420, 2011.

A. Savasere, E. Omiecinski, and S. B. Navathe, An efficient algorithm for mining association rules in large databases, Proceedings of International Conference on Very Large Data Bases (VLDB), pp.432-444, 1995.

W. Song, B. Yang, and Z. Xu, Index-bittablefi: An improved algorithm for mining frequent itemsets, Journal of Knowl. Based Syst, pp.507-513, 2008.

N. Jayalakshmi, V. Vidhya, M. Krishnamurthy, and A. Kannan, Frequent itemset generation using double hashing technique, Journal of Procedia Engineering, pp.1467-1478

M. Zaki, Scalable algorithms for association mining, IEEE Transactions on, vol.12, pp.372-390, 2000.

Y. Tsay and Y. Chang-chien, An efficient cluster and decomposition algorithm for mining association rules, Inf. Sci, vol.160, issue.1-4, pp.161-171, 2004.

N. Pasquier, Datamining: Algorithmes d'extraction et de réduction des règles d'association dans les bases de données, 2000.

R. Agrawal, T. Imielinski, and A. Swami, Database mining: a performance perspective, IEEE Transactions on Knowledge and Data Engineering, vol.5, issue.6, pp.914-925, 1993.

S. B. Yahia and E. M. Nguifo, Approches d'extraction de règles d'association basées sur la correspondance de galois, Ingénierie des Systèmes d'Information, pp.23-55, 2004.

A. J. Knobbe and E. K. Ho, Maximally informative k-itemsets and their efficient discovery, Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp.237-244, 2006.

C. Nevill-manning, G. Holmes, and I. H. Witten, The development of holte's 1r classifier, Artificial Neural Networks and Expert Systems, 1995. Proceedings., Second New Zealand International Two-Stream Conference on, pp.239-242, 1995.

J. R. Quinlan, Induction of decision trees, Mach. Learn, vol.1, pp.81-106, 1986.

S. Ruggieri, Knowledge and Data Engineering, IEEE Transactions on, pp.438-444, 2002.

D. Lowd and P. Domingos, Naive bayes models for probability estimation, Proceedings of the 22Nd International Conference on Machine Learning, ICML '05, pp.529-536, 2005.

C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, pp.273-297

W. W. Cohen and Y. Singer, Context-sensitive learning methods for text categorization, ACM Trans. Inf. Syst, vol.17, pp.141-173, 1999.

C. Aggarwal and C. Zhai, A survey of text classification algorithms, pp.163-222, 2012.

D. Landgrebe, A survey of decision tree classifier methodology, Systems, Man and Cybernetics, IEEE Transactions on, pp.660-674, 1991.

T. Evgeniou and M. Pontil, Support vector machines: Theory and applications, Machine Learning and Its Applications, pp.249-257, 2001.

V. Cherkassky, The nature of statistical learning theory, IEEE Trans. Neural Networks, vol.8, issue.6, p.1564, 1997.

S. Raschka, Naive bayes and text classification I-introduction and theory, CoRR, 2014.

M. Sewell, Ensemble learning, 2011.

L. Breiman, Random forests, Mach. Learn, vol.45, pp.5-32, 2001.

P. Komarek, Logistic Regression for Data Mining and High-Dimensional Classification, 2004.

L. Mitchell, T. M. Sloan, M. Mewissen, P. Ghazal, T. Forster et al., A parallel random forest classifier for r, Proceedings of the Second International Workshop on Emerging Computational Methods for the Life Sciences

I. H. Witten and E. Frank, Second Edition (Morgan Kaufmann Series in Data Management Systems), Data Mining: Practical Machine Learning Tools and Techniques, 2005.

M. Berry, Survey of Text Mining Clustering, Classification, and Retrieval, 2004.

Z. Qin, Introduction to e-commerce, 2009.

C. Loglisci, M. Berardi, S. D'alessandro, and P. Leo, Finding generalized closed frequent itemsets for mining non redundant association rules, Proceedings of the Fifteenth Italian Symposium on Advanced Database Systems, SEBD 2007, pp.17-20

A. Casali, R. Cicchetti, and L. Lakhal, Essential patterns: A perfect cover of frequent patterns, Proceedings of the 7th International Conference on Data Warehousing and Knowledge Discovery, DaWaK'05, pp.428-437, 2005.

G. Liu, J. Li, and L. Wong, A new concise representation of frequent itemsets using generators and a positive border, Knowl. Inf. Syst, vol.17, issue.1, pp.35-56, 2008.

T. Hamrouni, S. B. Yahia, and E. M. Nguifo, Sweeping the disjunctive search space towards mining new exact concise representations of frequent itemsets, Data Knowl. Eng, vol.68, issue.10, pp.1091-1111, 2009.
URL : https://hal.archives-ouvertes.fr/hal-02134384

P. Han and Y. , Mining frequent patterns without candidate generation, SIGMODREC: ACM SIGMOD Record, vol.29, 2000.

I. S. Galambos, Bonferroni-type Inequalities with Applications, 1996.

F. Bonchi and C. Lucchese, On condensed representations of constrained frequent patterns, Knowl. Inf. Syst, vol.9, issue.2, pp.180-201, 2006.

C. Aggarwal, Frequent pattern mining, 2014.

B. Ganter, G. Stumme, and R. Wille, Formal Concept Analysis, Foundations and Applications, vol.3626, 2005.

H. Mannila and H. Toivonen, Levelwise search and borders of theories in knowledge discovery, Data Min. Knowl. Discov, vol.1, issue.3, pp.241-258, 1997.

, Constraint-Based Mining and Inductive Databases, European Workshop on Inductive Databases and Constraint Based Mining, vol.3848, 2004.

D. Serrano and C. Antunes, Condensed representation of frequent itemsets, 18th International Database Engineering & Applications Symposium, pp.168-175, 2014.

E. Simoudis, J. Han, and U. M. Fayyad, Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), 1996.

Z. Liu, L. Hu, C. Wu, Y. Ding, Q. Wen et al., A novel process-based association rule approach through maximal frequent itemsets for big data processing, Future Generation Comp. Syst, pp.414-424, 2018.

M. L. Wong, W. Lam, and K. Leung, Using evolutionary programming and minimum description length principle for data mining of bayesian networks, IEEE Trans. Pattern Anal. Mach. Intell, vol.21, issue.2, pp.174-178, 1999.

J. Baixeries, C. Sacarea, and M. Ojeda-aciego, Formal Concept Analysis-13th International Conference, ICFCA 2015, vol.9113, 2015.

H. Liu, L. Liu, and H. Zhang, A fast pruning redundant rule method using galois connection, Appl. Soft Comput, pp.130-137, 2011.

U. M. Fayyad, G. Piatetsky-shapiro, and P. Smyth, Advances in knowledge discovery and data mining," ch. From Data Mining to Knowledge Discovery: An Overview, pp.1-34, 1996.

S. Benyahia, T. Hamrouni, and E. M. Nguifo, Frequent closed itemset based algorithms: a through structural and analytical survey, Journal of SIGKDD Explorations, pp.93-104, 2006.

N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, Efficient mining of association rules using closed itemset lattices, Inf. Syst, vol.24, issue.1, pp.25-46, 1999.

G. Stumme, R. Taouil, Y. Bastide, N. Pasquier, and L. Lakhal, Computing iceberg concept lattices with Titanic, Journal of Data Knowledge Engineering, pp.189-222, 2002.
URL : https://hal.archives-ouvertes.fr/hal-00578830

Y. Wang, Y. Jin, Y. Li, and K. Geng, DM data mining based on improved apriori algorithm, Information Computing and Applications-4th International Conference, ICICA 2013, pp.354-363, 2013.

J. Pei, J. Han, and R. Mao, CLOSET: an efficient algorithm for mining frequent closed itemsets, Workshop on Research Issues in Data Mining and Knowledge Discovery ACM SIGMOD'00, pp.21-30, 2000.

J. Wang, J. Han, and J. Pei, CLOSET+: searching for the best strategies for mining frequent closed itemsets, Proceedings of the 9th International Conference on Knowledge Discovery and Data Mining, KDD'03, pp.236-245, 2003.

G. Grahne and J. Zhu, Efficiently using prefix-trees in mining frequent itemsets, Proceedings of the 3rd Workshop on Frequent Itemset Mining Implementations, ICDM'03, pp.249-274, 2003.

M. J. Zaki and C. Hsiao, CHARM: an efficient algorithm for closed itemset mining, Proceedings of the 2nd International Conference on Data Mining SIAM'02, pp.457-473, 2002.

C. Lucchese, S. Orlando, and R. Perego, DCI closed: A fast and memory efficient algorithm to mine frequent closed itemsets, FIMI '04, Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, 2004.

T. Uno, T. Asai, Y. Uchida, and H. Arimura, LCM: an efficient algorithm for enumerating frequent closed item sets, Proceedings of The 3rd IEEE International Conference on Data Mining, ICDM'03, 2003.

C. Lucchese, S. Orlando, and R. Perego, Fast and memory efficient mining of frequent closed itemsets, Journal of IEEE Transactions on Knowledge and Data Engineering, pp.21-36, 2006.

T. Uno, T. Asai, Y. Uchida, and H. Arimura, An efficient algorithm for enumerating closed patterns in transaction databases, Proceedings of the 7th International Conference on Discovery Science, DS'04

T. M. Cover, Elements of information theory, 2006.

H. Heikinheimo, E. Hinkkanen, H. Mannila, T. Mielikäinen, and J. K. Seppänen, Finding low-entropy sets and trees from binary data, Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp.350-359, 2007.

S. Brin, R. Motwani, and C. Silverstein, Beyond market baskets: Generalizing association rules to correlations, SIGMOD Rec, vol.26, pp.265-276, 1997.

N. Tatti, Probably the best itemsets, Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.293-302, 2010.

C. Giannella, J. Han, J. Pei, X. Yan, and P. S. Yu, Mining frequent patterns in data streams at multiple time granularities, 2002.

W. Teng, M. Chen, and P. S. Yu, A regression-based temporal pattern mining scheme for data streams, Proceedings of International Conference on Very Large Data Bases (VLDB), pp.93-104, 2003.

C. Zhang and F. Masseglia, Discovering highly informative feature sets from data streams, Database and Expert Systems Applications, pp.91-104, 2010.

G. Chandrashekar and F. Sahin, A survey on feature selection methods, Computers and Electrical Engineering, vol.40, issue.1, pp.16-28, 2014.

I. Guyon and A. Elisseeff, An introduction to variable and feature selection, J. Mach. Learn. Res, vol.3, pp.1157-1182, 2003.

, Hadoop

, Casandra

R. Anand, Mining of massive datasets, 2012.

C. Lucchese, S. Orlando, and R. Perego, Parallel mining of frequent closed patterns: Harnessing modern computer architectures, Proceedings of the 7th IEEE International Conference on Data Mining (ICDM 2007), pp.242-251, 2007.

S. Wang, Y. Yang, Y. Gao, G. Chen, and Y. Zhang, Mapreduce-based closed frequent itemset mining with efficient redundancy filtering, 12th IEEE International Conference on Data Mining Workshops, ICDM Workshops, pp.449-453, 2012.

S. Moens, E. Aksehirli, and B. Goethals, Frequent itemset mining for big data, Proceedings of the 1st IEEE International Conference on Big Data, pp.111-118, 2013.

T. Hamrouni, S. Ben-yahia, and E. Mephu-nguifo, Succinct minimal generators: Theoretical foundations and applications, International journal of foundations of computer science, vol.19, issue.02, pp.271-296, 2008.
URL : https://hal.archives-ouvertes.fr/hal-02134392

T. Hamrouni, S. Ben-yahia, and E. M. Nguifo, Generalization of association rules through disjunction, Annals of Mathematics and Artificial Intelligence, vol.59, issue.2, pp.201-222, 2010.
URL : https://hal.archives-ouvertes.fr/hal-02134377

A. Gainaru, F. Cappello, S. Trausan-matu, and B. Kramer, Event log mining tool for large scale hpc systems, Proceedings of Euro-Par'11, 2011.

W. Xu, L. Huang, A. Fox, D. Patterson, and M. Jordan, Mining console logs for large-scale system problem detection, Proceedings of SysML'08, 2008.

G. Gasmi, S. Ben-yahia, E. M. Nguifo, and S. Bouker, Extraction of association rules based on literalsets, International Conference on Data Warehousing and Knowledge Discovery, pp.293-302, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00194525

S. Bouker, R. Saidi, S. Ben-yahia, and E. M. Nguifo, Ranking and selecting association rules based on dominance relationship, Tools with Artificial Intelligence (ICTAI), vol.1, pp.658-665, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00677853

S. Ayouni, S. Ben-yahia, and A. Laurent, Extracting compact and information lossless sets of fuzzy association rules, Fuzzy Sets and Systems, vol.183, issue.1, pp.1-25, 2011.
URL : https://hal.archives-ouvertes.fr/lirmm-00604420

S. Ben-yahia and E. M. Nguifo, Revisiting generic bases of association rules, International Conference on Data Warehousing and Knowledge Discovery, pp.58-67, 2004.

M. N. Jelassi, C. Largeron, and S. Ben-yahia, Efficient unveiling of multi-members in a social network, Journal of Systems and Software, vol.94, pp.30-38, 2014.
URL : https://hal.archives-ouvertes.fr/ujm-01017638

K. Chen, L. Zhang, S. Li, and W. Ke, Research on association rules parallel algorithm based on fp-growth, Proceedings of the International Conference on Information Computing and Applications, ICICA'11, pp.249-256, 2011.

S. Wang, Y. Yang, Y. Gao, G. Chen, and Y. Zhang, Mapreduce-based closed frequent itemset mining with efficient redundancy filtering, Proceedings of the 12th IEEE International Conference on Data Mining, pp.449-453, 2012.

S. Wang and L. Wang, An implementation of fp-growth algorithm based on high level data structures of weka-jung framework, Journal of Cases on Information Technology, pp.287-294, 2010.

C. Nègre, Efficient binary polynomial multiplication based on optimized karatsuba reconstruction, Journal of Cryptographic Engineering, pp.91-106, 2014.

A. Zanoni, Iterative toom-cook methods for very unbalanced long integer multiplication, Proceedings of the 35th International Symposium in Symbolic and Algebraic Computation, ISSAC'10, pp.319-323, 2010.

F. Gorunescu, Data Mining-Concepts, Models and Techniques, 2011.

S. Salah, R. Akbarinia, and F. Masseglia, Fast parallel mining of maximally informative k-itemsets in big data, Proceedings of the 2015 IEEE International Conference on Data Mining ICDM, pp.359-368, 2015.
URL : https://hal.archives-ouvertes.fr/lirmm-01187275

T. A. Runkler, Data Analytics-Models and Algorithms for Intelligent Data Analysis, 2016.

Y. Bassil, A survey on information retrieval, text categorization, and web crawling, 2012.

T. M. Cover and J. A. Thomas, Elements of information theory, 2006.

C. Zhang and F. Masseglia, Discovering highly informative feature sets from data streams, Proceedings of the 21st International Conference on Database and Expert Systems Applications, DEXA'10, pp.91-104, 2010.

O. Papapetrou, M. N. Garofalakis, and A. Deligiannakis, Sketching distributed sliding-window data streams, The VLDB Journal, 2015.

M. Zaharia, M. Chowdhury, M. J. Franklin, S. Shenker, and I. Stoica, Spark: Cluster computing with working sets, Proceedings of the 2Nd USENIX Conference on Hot Topics in Cloud Computing, HotCloud'10, pp.10-10, 2010.

I. Bouzouita, S. Elloumi, and S. Ben-yahia, Garc: A new associative classification approach, International Conference on Data Warehousing and Knowledge Discovery, pp.554-565, 2006.

T. Cover and P. Hart, Nearest neighbor pattern classification, IEEE Trans. Inf. Theor, pp.21-27, 2006.

F. Sebastiani, Machine learning in automated text categorization, Journal of ACM Comput. Surv, pp.1-47

T. Joachims, Text categorization with support vector machines: Learning with many relevant features, 1998.

J. D. Rennie, L. Shih, J. Teevan, and D. R. Karger, Tackling the poor assumptions of naive bayes text classifiers, Proceedings of the Twentieth International Conference on Machine Learning, pp.616-623, 2003.

J. Tang, S. Alelyani, and H. Liu, Feature selection for classification: A review, Data Classification: Algorithms and Applications, pp.37-64, 2014.

F. Ferjani, S. Elloumi, A. Jaoua, S. Ben-yahia, S. Ismail et al., Formal context coverage based on isolated labels: An efficient solution for text feature extraction, Information Sciences, vol.188, pp.198-214, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01300495

H. Brahmi, I. Brahmi, and S. Ben-yahia, Omc-ids: at the cross-roads of olap mining and intrusion detection, Pacific-Asia Conference on Knowledge Discovery and Data Mining PAKDD, pp.13-24, 2012.
URL : https://hal.archives-ouvertes.fr/hal-01300473

, Trec

S. Owen, Mahout in action, 2012.

, Nef cluster, 2015.

, English wikipedia articles, 2014.

, The clueweb09 dataset, 2009.