College
College of Science and TechnologyDepartment
Computer Systems TechnologyEducation
Ph.D.Information Technology / University of North Carolina at Charlotte
PostMasterCertificateAdvanced Databases/ Knowledge Discovery / The University of North Carolina at Charlotte
M.S.Computer Science / University of North Carolina at Charlotte
B.S.Software and Information Systems / The University of North Carolina at Charlotte
Research Interests
Knowledge Discovery and Data Mining; Multimedia Databases; Intelligent Web Search; Agent-Based Modeling and Complex Adaptive Systems; Heuristic Search.
Recent Publications
- Seung Hyun Im, Li-Shiang Tsay (2014). (Classifying an Object using Class Differentiators). (5) 2, pp. 64-71. Transactions on Machine Learning and Artificial Intelligence.
- Li-Shiang Tsay (2013). (On Objective Measures of Actionability in Knowledge Discovery). In Andrzej Skowron and Zbigniew Suraj, 43, pp. 559-575. Intelligent Systems Reference Library/Springer.
- Zbigniew Raś, Li-Shiang Tsay (2012). (Interesting Knowledge Mining). In Zbigniew W. Raś, and Li-Shiang Tsay, (3) 4, Inderscience Enterprises Ltd. .
- Li-Shiang Tsay (2010). (Advances in Intelligent Information Systems). (Studies in Computational Intelligence) 265, Springer.
- Z. Ras, Li-Shiang Tsay, A. Dardzinska (2009). (Tree-based Algorithms for Action Rules Discovery). In D. Zighed et al. , pp. 153-163. Mining Complex Data /Springer.
- Li-Shiang Tsay (2008). (Action rules mining). (2) pp. 1-5. Encyclopedia of Data Warehousing and Mining/Idea Group Inc..
- , Li-Shiang Tsay (2008). (Applications of agent-based modeling to pricing of reproducible information goods). (3) 44, pp. 725-739. Decision Support Systems.
- Li-Shiang Tsay (2008). (Tree-based construction of low-cost action rules). (1-2) 86, pp. 213-255. Fundamenta Informaticae Journal.
- Li-Shiang Tsay (2005). (Action rules). pp. 1-5. Encyclopedia of Data Warehousing and Minin/ Idea Group Inc..
- Li-Shiang Tsay (2005). (Action rules discovery: System DEAR2, method and experiments). 17, pp. 119-128. Journal of Experimental and Theoretical Artificial Intelligence/Taylor & Francis.