X. Amatriain, P. Josep, and M. , Data mining methods for recommender systems, Recommender systems handbook, pp.227-262, 2015.

I. Tuomi, Data is More Than Knowledge Implications of the Reversed Knowledge Hierarchy for Knowledge Management and Organizational Memory, Journal of Management Information Systems Fall, vol.16, issue.3, pp.107-121, 1999.

P. Ruiz, F. Potes, B. Kamsu, E. Grabot, and B. , Generating knowledge in maintenance from Experience Feedback. Knowledge-Based Systems, vol.68, pp.4-20, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01061658

J. W. Grzymala-busse, A new version of the rule induction system LERS, Fundamenta Informaticae, vol.31, issue.1, pp.27-39, 1997.

A. Chemchem and H. Drias, From data mining to knowledge mining: Application to intelligent agents, Expert Systems with Applications, vol.42, pp.1436-1445, 2015.

A. Chemchem, . Drias, . Habiba, and Y. Djenouri, Multilevel Clustering of Induction Rules: Application on Scalable Cognitive Agent, International Journal of Systems and Service-Oriented Engineering (IJSSOE), vol.4, pp.1-25, 2014.

V. Korde, C. Mahender, and . Namrata, Text classification and classifiers: A survey, International Journal of Artificial Intelligence & Applications, vol.3, issue.2, p.85, 2012.

M. Ikonomakis, . Kotsiantis, . Sotiris, and V. Tampakas, Text classification using machine learning techniques, WSEAS transactions on computers, vol.4, pp.966-974, 2005.

A. Jain and J. Mandowara, Text classification by combining text classifiers to improve the efficiency of classification, International Journal of Computer Application, vol.6, issue.2, pp.2250-1797, 2016.

J. Huang, J. Lu, L. Charles, and X. , Comparing naive Bayes, decision trees, and SVM with AUC and accuracy, Data Mining, 2003. ICDM 2003. Third IEEE International Conference on. IEEE, pp.553-556, 2003.

C. D. Manning, . Raghavan, . Prabhakar, . Schtze, and . Hinrich, Introduction to information retrieval, 2008.

I. Guyon, J. Weston, . Barnhill, and . Stephen, Gene selection for cancer classification using support vector machines. Machine learning, vol.46, pp.389-422, 2002.

V. Vapnik, The nature of statistical learning theory, Springer science and business media, 2013.

C. Hsu and C. Lin, A comparison of methods for multiclass support vector machines, IEEE transactions on Neural Networks, vol.13, issue.2, pp.415-425, 2002.

D. A. Adeniyi, Z. Wei, Y. Et, and Y. , Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method, Applied Computing and Informatics, vol.12, issue.1, pp.90-108, 2016.

X. Wu, . Kumar, Q. Vipin, and J. Ross, Top 10 algorithms in data mining, Knowledge and information systems, vol.14, issue.1, pp.1-37, 2008.

C. C. Aggarwal and C. Zhai, Mining text data, 2012.

C. C. Aggarwal, Data classification: algorithms and applications, 2014.

M. Anthimopoulos, . Christodoulidis, . Stergios, . Ebner, and . Lukas, Lung pattern classification for interstitial lung diseases using a deep convolutional neural network, IEEE transactions on medical imaging, vol.35, issue.5, pp.1207-1216, 2016.

A. Ossama, M. Jiang, and . Hui, Convolutional neural networks for speech recognition, IEEE/ACM Transactions on audio, vol.22, pp.1533-1545, 2014.

Y. Kim, Convolutional neural networks for sentence classification, 2014.

J. Mcauley, . John, and J. Leskovec, From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews, Proceedings of the 22nd international conference on World Wide Web, pp.897-908, 2013.

D. Dua and E. Karra-taniskidou, UCI Machine Learning Repository, 2017.

C. A. Irvine,