Association Rule Mining Bibliography

Journal articles

  1. L.T.T. Nguyen, B. Vo, T. Hong and H.C. Thanh. CAR-Miner: An efficient algorithm for mining class-association rules. Expert Systems with Applications, Volume 40, Issue 2 (2013), Page 2305-2311.
  2. K. Yang, T. Hong, Y-M. Chen and G. Lan. Projection-based partial periodic pattern mining for event sequences. Expert Systems with Applications, Volume 40, Issue 10 (2013), Page 4232-4240.
  3. C. Chen, G. Lan, T. Hong and Y. Lin. Mining high coherent association rules with consideration of support measure. Expert Systems with Applications, Volume 40, Issue 16 (2013), Page 6531-6537.
  4. B. Vo, T. Hong and B. Le. A lattice-based approach for mining most generalization association rules. Knowledge Based Systems, Volume 45, 2013, Page 20-30.
  5. L.T.T. Nguyen, B. Vo, T. Hong and H.C. Thanh. Classification based on association rules: A lattice-based approach. Expert Systems with Applications, Volume 39, Issue 13 (2012), Page 11357-11366.
  6. T. Hong and C. Wang. An efficient and effective association-rule maintenance algorithm for record modification. Expert Systems with Applications, Volume 37, Issue 1 (2010), Page 618-626.
  7. S-L. Wang, T. Lai, T. Hong and Y. Wu. Hiding collaborative recommendation association rules on horizontally partitioned data. Intelligent Data Analysis, Volume 14, Issue 1 (2010), Page 47-67.
  8. S-L. Wang, R. Maskey, A. Jafari and T. Hong. Efficient sanitization of informative association rules. Expert Systems with Applications, Volume 35, Issue 1-2 (2008), Page 442-450.
  9. S-L. Wang, D. Patel, A. Jafari and T. Hong. Hiding collaborative recommendation association rules. Appliend Intelligence, Volume 27, Issue 1 (2007), Page 67-77.
  10. C. Wang, S. Tseng, T. Hong and Y. Chu. Online Generation of Association Rules under Multi-dimensional Consideration Based on Negative-Border. Journal of Information Science and Engineering, Volume 23, Issue 1 (2007), Page 233-242.
  11. C. Wang, S. Tseng and T. Hong. Flexible online association rule mining based on multidimensional pattern relations. Information Sciences, Volume 176, Issue 12 (2006), Page 1752-1780.
  12. Y. Lee, T. Hong and W. Lin. Mining association rules with multiple minimum supports using maximum constraints. International Journal of Approximate Reasoning, Volume 40, Issue 1-2 (2005), Page 44-54.
  13. T. Hong, K. Lin and B. Chien. Mining Fuzzy Multiple-Level Association Rules from Quantitative Data. Applied Intelligence, Volume 18, Issue 1 (2003), Page 79-90.
  14. T. Hong, K. Lin and S-L. Wang. Fuzzy data mining for interesting generalized association rules. Fuzzy Sets and Systems, Volume 138, Issue 2 (2003), Page 255-269.
  15. T. Hong, C. Kuo and S. Chi. Trade-off Between Computation Time and Number of Rules for Fuzzy Mining from Quantitative Data. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, Volume 9, Issue 5 (2001), Page 587-604.
  16. T. Hong, C. Kuo and S. Chi. Mining association rules from quantitative data. Intelligent Data Analysis, Volume 3, Issue 5 (1999), Page 363-376.

Conference contributions

  1. G. Lan, T. Hong, P. Wu and S. Tsumoto. Mining hierarchical temporal association rules in a publication database, Proceedings of the IEEE 12th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2013, New York, NY, USA, pag: 503-508.
  2. L.T.T. Nguyen, B. Vo, T. Hong and H.C. Thanh. A fast algorithm for classification based on association rules, Proceedings of the IEEE International Conference on Granular Computing, GrC 2012, Hangzhou, China, pag: 365-369, ISBN 978-1-4673-2310-9.
  3. L.T.T. Nguyen, B. Vo, T. Hong and H.C. Thanh. Interestingness Measures for Classification Based on Association Rules, Proceedings of the 4th International Conference on Computational Collective Intelligence, Technologies and Applications, ICCCI 2012, Ho Chi Minh City, Vietnam, pag: 383-392, ISBN 978-3-642-34706-1.
  4. G. Lan, C. Chen, T. Hong and S. Lin. A fuzzy approach for mining general temporal association rules in a publication database, Proceedings of the 11th International Conference on Hybrid Intelligent Systems, HIS 2011, Melacca, Malaysia, pag: 611-615, ISBN 978-1-4577-2151-9.
  5. S-L. Wang, T. Hong, Y. Tsai and H. Kao. Hiding Sensitive Association Rules on Stars, Proceedings of the 2010 IEEE International Conference on Granular Computing, GrC 2010, San Jose, California, USA, pag: 505-508, ISBN 978-0-7695-4161-7.
  6. Y. Lee, T. Hong and C. Chen. Mining Generalized Association Rules with Quantitative Data under Multiple Support Constraints, Proceedings of the 2nd International Conference on Computational Collective Intelligence, Technologies and Applications, ICCCI 2010, Kaohsiung, Taiwan, pag: 224-231, ISBN 978-3-642-16731-7.
  7. S-L. Wang, T. Hong, Y. Tsai and H. Kao. Multi-table association rules hiding, Proceedings of the 10th International Conference on Intelligent Systems Design and Applications, ISDA 2010, Cairo, Egypt, pag: 1298-1302.
  8. S-L. Wang, T. Lai, T. Hong and Y. Wu. Hiding Predictive Association Rules on Horizontally Distributed Data, Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2009, Tainan, Taiwan, pag: 133-141, ISBN 978-3-642-02567-9.
  9. S-L. Wang, T. Lai, T. Hong and Y. Wu. Efficient Hiding of Collaborative Recommendation Association Rules with Updates, Proceedings of the 7th International Conference on Machine Learning and Applications, ICMLA 2008, San Diego, California, USA, pag: 737-740, ISBN 978-0-7695-3495-4.
  10. Y. Lee, T. Hong and T. Wang. Mining Generalized Association Rules from a Different Perspective, Proceedings of the 4th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2007, Haikou, Hainan, China, pag: 351-355.
  11. Y. Lee, T. Hong and T. Wang. Mining Multiple-Level Association Rules Under the Maximum Constraint of Multiple Minimum Supports, Proceedings of the 19th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2006, Annecy, France, pag: 1329-1338, ISBN 3-540-35453-0.
  12. C. Huang, T. Hong and S. Horng. Simultaneously Mining Fuzzy Inter- and Intra-Object Association Rules, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2006, Taipei, Taiwan, pag: 2778-2783, ISBN 1-4244-0099-6.
  13. Y. Lee, T. Hong and T. Wang. Mining Fuzzy Multiple-level Association Rules under Multiple Minimum Supports, Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2006, Taipei, Taiwan, pag: 4112-4117, ISBN 1-4244-0099-6.
  14. C. Wang, S. Tseng, T. Hong and Y. Chu. A Three-phased Online Association Rule Mining Approach for Diverse Mining Requests, Proceedings of the 4th International Conference on Electronic Business, ICEB 2004, Beijing, China, pag: 1085-1090, ISBN 7-5062-7342-X.
  15. Y. Lee, T. Hong and W. Lin. Mining Fuzzy Association Rules with Multiple Minimum Supports Using Maximum Constraints, Proceedings of the IEEE Internation Conference on Systems, Man and Cybernetics, SMC 2004, The Hague, The Netherlands, pag: 3140-3145.
  16. S-L. Wang, M. Wang, W. Lin and T. Hong. Adjustable discovery of adaptive-support association rules for collaborative recommendation systems, Proceedings of the IEEE Internation Conference on Systems, Man and Cybernetics, SMC 2004, The Hague, The Netherlands, pag: 3250-3254.
  17. S-L. Wang, M. Wang, W. Lin and T. Hong. Efficient generation of Adaptive-Support Association Rules, Proceedings of the IEEE Internation Conference on Systems, Man and Cybernetics, SMC 2003, Washington, DC, USA, pag: 894-899.