D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 30 Citations 3,315 131 World Ranking 8874 National Ranking 90

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • The Internet

Unil Yun focuses on Data mining, Scalability, Data stream mining, Artificial intelligence and Tree structure. His work on Knowledge extraction is typically connected to Process as part of general Data mining study, connecting several disciplines of science. His work in Data stream mining addresses subjects such as Field, which are connected to disciplines such as Efficient algorithm and Web service.

The various areas that he examines in his Artificial intelligence study include Machine learning and Pattern recognition. His work deals with themes such as Utility mining and Theoretical computer science, which intersect with Tree structure. His Pruning study incorporates themes from Association rule learning, Apriori algorithm and Minimum weight.

His most cited work include:

  • WFIM: Weighted Frequent Itemset Mining with a weight range and a minimum weight. (119 citations)
  • High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates (100 citations)
  • Efficient mining of weighted interesting patterns with a strong weight and/or support affinity (81 citations)

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

Unil Yun mainly focuses on Data mining, Scalability, Artificial intelligence, Pruning and Pattern recognition. His research integrates issues of Tree and Tree structure in his study of Data mining. His Scalability research integrates issues from Window, Database transaction and Correctness.

His studies in Artificial intelligence integrate themes in fields like Field and Machine learning. Unil Yun interconnects Knowledge extraction and Cluster analysis in the investigation of issues within Pattern recognition. His work on Apriori algorithm as part of general Association rule learning study is frequently linked to Constraint, bridging the gap between disciplines.

He most often published in these fields:

  • Data mining (60.66%)
  • Scalability (22.95%)
  • Artificial intelligence (21.31%)

What were the highlights of his more recent work (between 2019-2021)?

  • Data mining (60.66%)
  • Scalability (22.95%)
  • Pruning (12.57%)

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

His primary scientific interests are in Data mining, Scalability, Pruning, Structure and Artificial intelligence. His Data mining research incorporates elements of Efficient algorithm, The Internet and Fuzzy logic. His studies in Scalability integrate themes in fields like Window and Sliding window protocol.

He has included themes like Machine learning and Pattern recognition in his Artificial intelligence study. His Pattern recognition research integrates issues from Recurrent neural network and Cluster analysis. His studies deal with areas such as Tree, Tree structure, Dynamic data and Table as well as Data stream mining.

Between 2019 and 2021, his most popular works were:

  • ASRNN: A recurrent neural network with an attention model for sequence labeling (18 citations)
  • Efficient approach for incremental weighted erasable pattern mining with list structure (11 citations)
  • A Pre-large Weighted-Fusion System of Sensed High-Utility Patterns (9 citations)

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

  • Artificial intelligence
  • Machine learning
  • The Internet

Data mining, Database transaction, Dynamic database, Pruning and Scalability are his primary areas of study. His work in the fields of Data mining, such as Episode mining, intersects with other areas such as Event sequence. His Database transaction research incorporates themes from Window and The Internet.

His research brings together the fields of Feature and Pruning. His work deals with themes such as Association rule learning and Fuzzy logic, which intersect with Efficient algorithm. His Transaction data study, which is part of a larger body of work in Database, is frequently linked to Profit, Multi-core processor, Limit and Process, bridging the gap between disciplines.

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

WFIM: Weighted Frequent Itemset Mining with a weight range and a minimum weight.

Unil Yun;John J. Leggett.
siam international conference on data mining (2005)

188 Citations

High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates

Unil Yun;Heungmo Ryang;Keun Ho Ryu.
Expert Systems With Applications (2014)

152 Citations

Efficient mining of weighted interesting patterns with a strong weight and/or support affinity

Unil Yun.
Information Sciences (2007)

127 Citations

Sliding window based weighted maximal frequent pattern mining over data streams

Gangin Lee;Unil Yun;Keun Ho Ryu.
Expert Systems With Applications (2014)

119 Citations

Top- k high utility pattern mining with effective threshold raising strategies

Heungmo Ryang;Unil Yun.
Knowledge Based Systems (2015)

104 Citations

Efficient frequent pattern mining based on Linear Prefix tree

Gwangbum Pyun;Unil Yun;Keun Ho Ryu.
Knowledge Based Systems (2014)

104 Citations

A new framework for detecting weighted sequential patterns in large sequence databases

Unil Yun.
Knowledge Based Systems (2008)

96 Citations

WSpan: Weighted Sequential pattern mining in large sequence databases

Unil Yun;John J. Leggett.
2006 3rd International IEEE Conference Intelligent Systems (2006)

94 Citations

Damped window based high average utility pattern mining over data streams

Unil Yun;Donggyu Kim;Eunchul Yoon;Hamido Fujita.
Knowledge Based Systems (2017)

88 Citations

Mining maximal frequent patterns by considering weight conditions over data streams

Unil Yun;Gangin Lee;Keun Ho Ryu.
Knowledge Based Systems (2014)

85 Citations

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Best Scientists Citing Unil Yun

Jerry Chun-Wei Lin

Jerry Chun-Wei Lin

Western Norway University of Applied Sciences

Publications: 100

Philippe Fournier-Viger

Philippe Fournier-Viger

Harbin Institute of Technology

Publications: 82

Tzung-Pei Hong

Tzung-Pei Hong

National University of Kaohsiung

Publications: 55

Gautam Srivastava

Gautam Srivastava

Brandon University

Publications: 41

Philip S. Yu

Philip S. Yu

University of Illinois at Chicago

Publications: 27

Han-Chieh Chao

Han-Chieh Chao

National Dong Hwa University

Publications: 22

Vincent S. Tseng

Vincent S. Tseng

National Yang Ming Chiao Tung University

Publications: 18

Witold Pedrycz

Witold Pedrycz

University of Alberta

Publications: 17

Xindong Wu

Xindong Wu

Hefei University of Technology

Publications: 13

Carson Kai-Sang Leung

Carson Kai-Sang Leung

University of Manitoba

Publications: 13

Young-Koo Lee

Young-Koo Lee

Kyung Hee University

Publications: 9

Sung Wook Baik

Sung Wook Baik

Sejong University

Publications: 6

Hamido Fujita

Hamido Fujita

Association for Computing Machinery

Publications: 6

Joao P. S. Catalao

Joao P. S. Catalao

University of Porto

Publications: 6

Beibei Wang

Beibei Wang

University of Maryland, College Park

Publications: 6

Jerry Chun-Wei Lin

Jerry Chun-Wei Lin

Western Norway University of Applied Sciences

Publications: 5

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