His main research concerns Data mining, Artificial intelligence, Association rule learning, Fuzzy set and Database transaction. In general Data mining study, his work on FSA-Red Algorithm often relates to the realm of Process, thereby connecting several areas of interest. His Artificial intelligence study incorporates themes from Tournament, Machine learning, Series and Computer Go.
His Association rule learning research is multidisciplinary, incorporating elements of Fuzzy association rules, Tree structure, Lattice and Process. His Fuzzy set research includes elements of Transaction data and Algorithm design. The concepts of his Database transaction study are interwoven with issues in Scalability, Information sensitivity, Knowledge extraction and Fitness function.
His scientific interests lie mostly in Data mining, Artificial intelligence, Fuzzy logic, Association rule learning and Fuzzy set. His work deals with themes such as Pruning, Database transaction, Tree, Fuzzy classification and Fuzzy set operations, which intersect with Data mining. His studies deal with areas such as Defuzzification and Membership function as well as Fuzzy classification.
He interconnects Stability, Genetic algorithm, Machine learning and Pattern recognition in the investigation of issues within Artificial intelligence. The Fuzzy logic study which covers Mathematical optimization that intersects with Scheduling and Algorithm. His biological study spans a wide range of topics, including Transaction data, Algorithm design, Data mining algorithm and FSA-Red Algorithm.
Tzung-Pei Hong mostly deals with Data mining, Artificial intelligence, Pruning, Database transaction and Fuzzy logic. A large part of his Data mining studies is devoted to Association rule learning. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Combinatorial explosion and Pattern recognition.
His Pruning study integrates concerns from other disciplines, such as Tree, Space, Sequence and Fast algorithm. In his work, Knowledge extraction is strongly intertwined with Algorithm, which is a subfield of Database transaction. His work carried out in the field of Fuzzy logic brings together such families of science as Utility mining, Computational intelligence and Cluster analysis.
His primary areas of investigation include Data mining, Database transaction, Scalability, Pruning and Association rule learning. His Data mining study combines topics from a wide range of disciplines, such as Space, Structure, Artificial intelligence, Efficient algorithm and Speedup. Tzung-Pei Hong has researched Artificial intelligence in several fields, including Machine learning and Combinatorial explosion.
His research integrates issues of Knowledge extraction and Skyline in his study of Database transaction. The Pruning study combines topics in areas such as Element and Fast algorithm. His Association rule learning research integrates issues from Fuzzy number, Fuzzy set, Fuzzy classification, Defuzzification and Chromosome.
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Mining association rules with multiple minimum supports using maximum constraints
Yeong-Chyi Lee;Tzung-Pei Hong;Wen-Yang Lin.
International Journal of Approximate Reasoning (2005)
Induction of fuzzy rules and membership functions from training examples
Tzung-Pei Hong;Chai-Ying Lee.
Fuzzy Sets and Systems (1996)
Mining association rules from quantitative data
Tzung-Pei Hong;Chan-Sheng Kuo;Sheng-Chai Chi.
intelligent data analysis (1999)
TRADE-OFF BETWEEN COMPUTATION TIME AND NUMBER OF RULES FOR FUZZY MINING FROM QUANTITATIVE DATA
Tzung-Pei Hong;Chan-Sheng Kuo;Sheng-Chai Chi.
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems (2001)
Integrating fuzzy knowledge by genetic algorithms
Ching-Hung Wang;Tzung-Pei Hong;Shian-Shyong Tseng.
IEEE Transactions on Evolutionary Computation (1998)
Fuzzy data mining for interesting generalized association rules
Tzung-Pei Hong;Kuei-Ying Lin;Shyue-Liang Wang.
Fuzzy Sets and Systems (2003)
Incrementally fast updated frequent pattern trees
Tzung-Pei Hong;Chun-Wei Lin;Yu-Lung Wu.
Expert Systems With Applications (2008)
Finding relevant attributes and membership functions
Tzung-Pei Hong;Jyh-Bin Chen.
Fuzzy Sets and Systems (1999)
The Computational Intelligence of MoGo Revealed in Taiwan's Computer Go Tournaments
Chang-Shing Lee;Mei-Hui Wang;G. Chaslot;J.-B. Hoock.
IEEE Transactions on Computational Intelligence and AI in Games (2009)
An effective tree structure for mining high utility itemsets
Chun-Wei Lin;Tzung-Pei Hong;Wen-Hsiang Lu.
Expert Systems With Applications (2011)
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