Darapisut, S., Amphawan, K., Rimcharoen, S. & Leelathakul, N. (2022). N-Most Interesting Location-based Recommender System. ECTI Transactions on computer and Information Technology, 16(1), 84-99.
Patipat, P., Meepradit, P., Amphawan, K., Meepradit, Y. (2022). Development of a Recommendation System for Occupational Safety, Health and Environmental Management for Higher Education Institutions in Thailand. Thai Journal of Public Health, 52(2), 113-120.
NILR: N-Most Interesting Location-based Recommender System, In SMA-2020, Jeju, South Korea, Sep 17-18, 2020
An Improvement of supplementary book suggestion system, SMA-2020
P. Kamlangpuech and K. Amphawan, A new system for analyzing contents of Computer Science courses, In ICAICTA-2020: Notify July 24, 2020
Klangwisan, K., & Amphawan, K. (2017). Mining weighted-frequent-regular Itemsets from transactional database. In Proceedings of 9th International Conference on Knowledge and Smart Technologies 2017 (pp. 66-71). Pattaya: Thailand.
Laoviboon, S., & Amphawan, K. (2017). Mining High-Utility Itemsets with Irregular Occurrence. In Proceedings of 9th International Conference on Knowledge and Smart Technologies 2017 (pp. 89-94). Pattaya: Thailand.
Eisariyodom, S., & Amphawan, K. (2017). Discovering interesting itemsets based on change in regularity of occurrence. In Proceedings of 9th International Conference on Knowledge and Smart Technologies 2017 (pp. 138-143). Pattaya: Thailand.
K. Amphawan, J.Soulas, P. Lenca, “Mining top-k Episodes from Sensor Streams”, Procedia Computer Science(The 7th International Conference on Advances in Information Technology), vol. 69, pp.76-85, 2015.
K. Amphawan, A. Surarerks, “Pushing Regularity Constraint on High Utility Itemsets Mining”, The 2015 International Conference on Advanced Informatics: Concepts, Theory And Application (ICAICIT2015) [Best paper award]
K. Amphawan , P. Lenca, “Mining top-k frequent-regular closed patterns”, Expert Systems with Applications, Elsevier, vol. 42(21), pp. 7882-7894, 2015.
K. Amphawan, P.Sittichaitaweekul, “Mining top-k frequent-regular patterns based on user-given length constraints”, The 19th International Annual Symposium on Computational Science and Engineering
S. Chompaisal, K. Amphawan, A. Surarerks, “Mining N-most Interesting Multi-level Frequent Itemsets without Support Threshold”, Proceedings of Recent Advances in Information and Communication Technology.
P. Sittichaitaweekul, K. Amphawan, “Enhancing quality of results on Top-k Frequent-Regular Pattern mining”, Proceedings of International Conference on Engineering Science and Innovative Technology.
K. Amphawan and P. Lenca, “Mining top-k frequent-regular patterns based on user-given trade-off between frequency and regularity”, Proceeding of the 6th International Conference on Advances in Information Technology: IAIT-2013, Bangkok, Thailand, Springer.
K. Amphawan, “SST: An efficient suffix-sharing trie structure for dictionary lookup”, Proceedings of 7th Asia international conference on mathematical modeling and computer simulation.
K. Amphawan and A. Surarerks, “An efficient method for constructing dictionary based on decompounding words technique”, The 17th International Annual Symposium on Computational Science and Engineering.
K. Amphawan, P. Lenca, and A. Surarerks, “Mining top-k regular-frequent itemsets using database partitioning and support estimation”, Expert Systems with Applications, Volume 39, Issue 2, February 1, Pages 1924-1936.
K. Amphawan K, P. Lenca , and A. Surarerks, “Efficient mining top-k regular-frequent itemset using compressed tidsets”, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 7104 LNAI , pp. 124-135.
K. Amphawan, A. Surarerks and P. Lenca, “Mining periodic-frequent itemset with approximate periodicity using interval transaction-ids list tree”. Proceeding of The 3rd International Conference on Knowledge Discovery and Data Mining: WKDD 2010, Phuket, Thailand, January 9-10.
K. Amphawan, P. Lenca and A. Surarerks, “Mining top-k periodic-frequent pattern from transactional databases without support threshold”. Proceeding of the 3rd International Conference on Advances in Information Technology: IAIT-09, Bangkok, Thailand, December 1-5, ser. CCIS, vol. 55. Springer, pp. 18-29.
K. Amphawan and A. Surarerks, “Mining association rule using an improvement of frequent item tree “. Proceeding of the 2nd Joint Conference of Science and Software Engineering: JCSSE2005, Burapha University, Chonburi, Thailand, November 17-18.
K. Amphawan and A. Surarerks, “An approach of frequent item tree for association generation”. Proceedings of Artificial Intelligence and Soft Computing (ASC), Benidorm, Spain.
K. Amphawan and A. Surarerks, “Mining Association Rules with Frequent Item Tree”. Proceedings of the 8th National Computer Science and Engineering Conference:NCSEC2004, Songkhla, Thailand, October 21-22.
