H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 59 Citations 13,323 573 World Ranking 1669 National Ranking 9

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Data mining

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 most cited work include:

  • Induction of fuzzy rules and membership functions from training examples (333 citations)
  • Mining association rules from quantitative data (242 citations)
  • TRADE-OFF BETWEEN COMPUTATION TIME AND NUMBER OF RULES FOR FUZZY MINING FROM QUANTITATIVE DATA (169 citations)

What are the main themes of his work throughout his whole career to date?

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.

He most often published in these fields:

  • Data mining (80.31%)
  • Artificial intelligence (29.99%)
  • Fuzzy logic (24.12%)

What were the highlights of his more recent work (between 2015-2021)?

  • Data mining (80.31%)
  • Artificial intelligence (29.99%)
  • Pruning (14.08%)

In recent papers he was focusing on the following fields of study:

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.

Between 2015 and 2021, his most popular works were:

  • A survey of incremental high-utility itemset mining (78 citations)
  • Efficient algorithms for mining high-utility itemsets in uncertain databases (73 citations)
  • Mining frequent itemsets using the N-list and subsume concepts (58 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Data mining

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.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

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)

1452 Citations

Induction of fuzzy rules and membership functions from training examples

Tzung-Pei Hong;Chai-Ying Lee.
Fuzzy Sets and Systems (1996)

510 Citations

Mining association rules from quantitative data

Tzung-Pei Hong;Chan-Sheng Kuo;Sheng-Chai Chi.
intelligent data analysis (1999)

320 Citations

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)

223 Citations

Integrating fuzzy knowledge by genetic algorithms

Ching-Hung Wang;Tzung-Pei Hong;Shian-Shyong Tseng.
IEEE Transactions on Evolutionary Computation (1998)

222 Citations

Fuzzy data mining for interesting generalized association rules

Tzung-Pei Hong;Kuei-Ying Lin;Shyue-Liang Wang.
Fuzzy Sets and Systems (2003)

220 Citations

Incrementally fast updated frequent pattern trees

Tzung-Pei Hong;Chun-Wei Lin;Yu-Lung Wu.
Expert Systems With Applications (2008)

215 Citations

Finding relevant attributes and membership functions

Tzung-Pei Hong;Jyh-Bin Chen.
Fuzzy Sets and Systems (1999)

199 Citations

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)

198 Citations

An effective tree structure for mining high utility itemsets

Chun-Wei Lin;Tzung-Pei Hong;Wen-Hsiang Lu.
Expert Systems With Applications (2011)

193 Citations

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