H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 58 Citations 10,211 261 World Ranking 1827 National Ranking 10

Research.com Recognitions

Awards & Achievements

2019 - ACM Distinguished Member

2015 - ACM Senior Member

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Data mining

Vincent S. Tseng spends much of his time researching Data mining, Efficient algorithm, Process, Pruning and Database transaction. Vincent S. Tseng combines subjects such as Data stream, Machine learning, Utility mining and Artificial intelligence with his study of Data mining. His studies examine the connections between Pruning and genetics, as well as such issues in Measure, with regards to Timestamp.

His Database transaction research is multidisciplinary, incorporating perspectives in Tree, Time complexity and Task. His work in Algorithm design addresses subjects such as Knowledge extraction, which are connected to disciplines such as Mobile computing. His work on Scalability as part of general Database research is frequently linked to Tuple, Command-line interface and Implementation, thereby connecting diverse disciplines of science.

His most cited work include:

  • Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases (342 citations)
  • SPMF: a Java open-source pattern mining library (287 citations)
  • UP-Growth: an efficient algorithm for high utility itemset mining (265 citations)

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

His primary areas of investigation include Data mining, Artificial intelligence, Machine learning, Association rule learning and Cluster analysis. His biological study spans a wide range of topics, including Scalability, Fuzzy set, Fuzzy logic and Database transaction, Utility mining. His Scalability research is multidisciplinary, relying on both Video tracking, Wireless sensor network and Big data.

His Artificial intelligence study frequently links to related topics such as Pattern recognition. As part of his studies on Machine learning, Vincent S. Tseng often connects relevant areas like Field. Vincent S. Tseng has researched Association rule learning in several fields, including Algorithm design and Data stream mining.

He most often published in these fields:

  • Data mining (54.44%)
  • Artificial intelligence (23.67%)
  • Machine learning (16.27%)

What were the highlights of his more recent work (between 2016-2021)?

  • Artificial intelligence (23.67%)
  • Machine learning (16.27%)
  • Data mining (54.44%)

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

His primary scientific interests are in Artificial intelligence, Machine learning, Data mining, Deep learning and Data science. His research integrates issues of Medical record and Receiver operating characteristic in his study of Artificial intelligence. His research in Data mining intersects with topics in Scalability, Sliding window protocol, Database transaction, Task and Efficient algorithm.

His study in Scalability is interdisciplinary in nature, drawing from both Sequential Pattern Mining, Measure and Pruning. In his work, Database is strongly intertwined with Speedup, which is a subfield of Database transaction. Vincent S. Tseng has included themes like Time complexity and Key in his Efficient algorithm study.

Between 2016 and 2021, his most popular works were:

  • Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis (152 citations)
  • EFIM: a fast and memory efficient algorithm for high-utility itemset mining (77 citations)
  • A Survey of Utility-Oriented Pattern Mining (47 citations)

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

  • Artificial intelligence
  • Machine learning
  • Algorithm

Artificial intelligence, Machine learning, Database, Data mining and Database transaction are his primary areas of study. His work in Artificial intelligence tackles topics such as Multivariate statistics which are related to areas like Biomedicine and Rendering. Many of his research projects under Database are closely connected to MEDLINE with MEDLINE, tying the diverse disciplines of science together.

His Data mining research is multidisciplinary, incorporating elements of Scalability and Computational intelligence. Vincent S. Tseng has included themes like Range, Sequential Pattern Mining, The Internet and Measure in his Scalability study. In his research on the topic of Database transaction, Time complexity, Task and Key is strongly related with Efficient algorithm.

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

Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases

V. S. Tseng;Bai-En Shie;Cheng-Wei Wu;P. S. Yu.
IEEE Transactions on Knowledge and Data Engineering (2013)

501 Citations

SPMF: a Java open-source pattern mining library

Philippe Fournier-Viger;Antonio Gomariz;Ted Gueniche;Azadeh Soltani.
Journal of Machine Learning Research (2014)

450 Citations

UP-Growth: an efficient algorithm for high utility itemset mining

Vincent S. Tseng;Cheng-Wei Wu;Bai-En Shie;Philip S. Yu.
knowledge discovery and data mining (2010)

424 Citations

Semantic trajectory mining for location prediction

Josh Jia-Ching Ying;Wang-Chien Lee;Tz-Chiao Weng;Vincent S. Tseng.
advances in geographic information systems (2011)

343 Citations

FHM: Faster High-Utility Itemset Mining using Estimated Utility Co-occurrence Pruning

Philippe Fournier-Viger;Cheng-Wei Wu;Souleymane Zida;Vincent S. Tseng.
international syposium on methodologies for intelligent systems (2014)

332 Citations

Mining user similarity from semantic trajectories

Josh Jia-Ching Ying;Eric Hsueh-Chan Lu;Wang-Chien Lee;Tz-Chiao Weng.
workshop on location-based social networks (2010)

248 Citations

Efficient Relevance Feedback for Content-Based Image Retrieval by Mining User Navigation Patterns

Ja-Hwung Su;Wei-Jyun Huang;Philip S Yu;Vincent S Tseng.
IEEE Transactions on Knowledge and Data Engineering (2011)

225 Citations

Music Recommendation Using Content and Context Information Mining

Ja-Hwung Su;Hsin-Ho Yeh;P.S. Yu;V.S. Tseng.
IEEE Intelligent Systems (2010)

216 Citations

Accurate Classification of Diminutive Colorectal Polyps Using Computer-Aided Analysis

Peng Jen Chen;Meng Chiung Lin;Mei Ju Lai;Jung Chun Lin.
Gastroenterology (2018)

206 Citations

An efficient algorithm for mining temporal high utility itemsets from data streams

Chun-Jung Chu;Vincent S. Tseng;Tyne Liang.
Journal of Systems and Software (2008)

202 Citations

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Best Scientists Citing Vincent S. Tseng

Jerry Chun-Wei Lin

Jerry Chun-Wei Lin

Western Norway University of Applied Sciences

Publications: 169

Philippe Fournier-Viger

Philippe Fournier-Viger

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Tzung-Pei Hong

Tzung-Pei Hong

National University of Kaohsiung

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Unil Yun

Unil Yun

Sejong University

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Han-Chieh Chao

Han-Chieh Chao

National Dong Hwa University

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Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

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Gautam Srivastava

Gautam Srivastava

Brandon University

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Hamido Fujita

Hamido Fujita

Association for Computing Machinery

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Wen-Chih Peng

Wen-Chih Peng

National Yang Ming Chiao Tung University

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Jerry Chun-Wei Lin

Jerry Chun-Wei Lin

Western Norway University of Applied Sciences

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Ning Zhong

Ning Zhong

Maebashi Institute of Technology

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Carson Kai-Sang Leung

Carson Kai-Sang Leung

University of Manitoba

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Longbing Cao

Longbing Cao

University of Technology Sydney

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Hui Xiong

Hui Xiong

Rutgers, The State University of New Jersey

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Michael B. Wallace

Michael B. Wallace

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César Hervás-Martínez

César Hervás-Martínez

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