1. Gabroveanu, M., Diaconescu, I. M., Extracting Semantic Annotations from Moodle Data, Proceedings of the 2nd East European Workshop on Rule-Based Applications (RuleApps 2008) at the 18th European Conference on Artificial Intelligence (ECAI 2008), pp. 1-5, Patras, Greece, (2008).
2. Agrawal, R., Imielinski, T., Swami, A. N., Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, Buneman, P., Jajodia, S. eds., ACM Press, pp. 207-216, Washington, D.C., May 26-28, (1993).10.1145/170036.170072
4. Agrawal, R., Srikant, R., Fast algorithms for mining association rules, Proceedings of the 20th International Conference Very Large Data Bases, (VLDB), Morgan Kaufmann, pp. 487-499, (1994).
5. Park, J. S., Chen, M. S., Yu, P. S., An effective hash-based algorithm for mining association rules, Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, (SIGMOD '95), pp. 175-186, San Jose, Canada, (1995).10.1145/223784.223813
6. Savasere, A., Omiecinski, E., Navathe, S. B., An efficient algorithm for mining association rules in large databases, Proceedings of 21th International Conference on Very Large Data Bases (VLDB’95), Dayal, U. Gray, P. M. D., Nishio, S. eds., Morgan Kaufmann, pp. 432-444, (1995).
7. Brin, S., Motwani, R., Ullman, J. D., Tsur, S., Dynamic itemset counting and implication rules for market basket data, Proceedings ACM SIGMOD International Conference on Management of Data, ACM Press, pp. 255-264, (1997).10.1145/253262.253325
8. Agrawal, R., Shafer, J. C., Parallel mining of association rules, IEEE Transactions on Knowledge And Data Engineering, vol. 8, pp. 962–969, (1996).10.1109/69.553164
9. Cheung, D. W., Han, J., Ng, V. T., Fu, A. W., Fu, Y., A fast distributed algorithm for mining association rules, Proceedings of the 4th International Conference on Parallel and Distributed Information Systems (PDIS ’96), IEEE Computer Society Technical Committee on Data Engineering, and ACM SIGMOD, pp. 31–43, (1996).
10. Li, L., Zhang, M., The strategy of mining association rule based on cloud computing, Proceedings of the International Conference on Business Computing and Global Informatization, (BCGIN ’11), IEEE Computer Society, pp. 475–478, Shanghai, (2011).10.1109/BCGIn.2011.125
12. Lin, M. Y., Lee, P. Y., Hsueh, S. C., Apriori-based frequent itemset mining algorithms on MapReduce, Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication (ICUIMC '12), ACM, pp. 76:1–76:8, New York, NY, USA (2012).
14. Garcia, E., Romero, C., Ventura, S., Calders, T., Drawbacks and solutions of applying association rule mining in learning management systems, Proceedings of the International Workshop on Applying Data Mining in e-Learning (ADML’07), pp. 13-22, Crete, Greece, (2007).
18. Cyganiak, R., Wood, D., Lanthaler, M., RDF 1.1 Concepts and Abstract Syntax, W3C Recommendation, 25 February 2014, http://www.w3.org/TR/rdf-concepts/, (2014).