World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
44
Citations
5851
World Ranking
7710
National Ranking
76

Overview

Unil Yun is affiliated with Sejong University in South Korea and has contributed extensively to the field of computer science, with a notable focus on artificial intelligence and information systems. Their research output comprises 240 publications within computer science, including 81 in artificial intelligence and 73 in information systems, among other subfields such as computational theory and mathematics, signal processing, and computer networks and communications.

Their research interests cover a range of topics, emphasizing areas such as data mining algorithms and applications, rough sets and fuzzy logic, and imbalanced data classification techniques. Additional specialization includes data management and algorithms, data stream mining techniques, advanced database systems and queries, and topic modeling.

Unil Yun's frequent collaborators include Jerry Chun-Wei Lin, Bay Vo, Hanju Kim, Heonho Kim, and Myungha Cho. These coauthors reflect sustained partnerships, contributing to the scientist's broad research network.

Some of the recent papers authored or coauthored by Unil Yun are:

  • ASRNN: A recurrent neural network with an attention model for sequence labeling (2020, Knowledge-Based Systems)
  • A predictive GA-based model for closed high-utility itemset mining (2021, Applied Soft Computing)
  • Bot2Vec: A general approach of intra-community oriented representation learning for bot detection in different types of social networks (2021, Information Systems)
  • Fuzzy high-utility pattern mining in parallel and distributed Hadoop framework (2020, Information Sciences)
  • Efficient list based mining of high average utility patterns with maximum average pruning strategies (2020, Information Sciences)

Unil Yun has published predominantly in journals such as Knowledge-Based Systems, Information Sciences, Applied Intelligence, IEEE Access, and Expert Systems with Applications. The distribution of publications across these venues indicates a consistent engagement with journals focusing on applied and theoretical aspects of intelligent systems and data science.

Best Publications

  • ASRNN: A recurrent neural network with an attention model for sequence labeling

    Jerry Chun-Wei Lin;Jerry Chun-Wei Lin;Yinan Shao;Youcef Djenouri;Unil Yun

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

    Unil Yun;John J. Leggett

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

    Unil Yun;Heungmo Ryang;Keun Ho Ryu

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

    Heungmo Ryang;Unil Yun

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

    Unil Yun

  • Sliding window based weighted maximal frequent pattern mining over data streams

    Gangin Lee;Unil Yun;Keun Ho Ryu

  • Damped window based high average utility pattern mining over data streams

    Unil Yun;Donggyu Kim;Eunchul Yoon;Hamido Fujita

  • Efficient frequent pattern mining based on Linear Prefix tree

    Gwangbum Pyun;Unil Yun;Keun Ho Ryu

  • High utility pattern mining over data streams with sliding window technique

    Heungmo Ryang;Unil Yun

  • WSpan: Weighted Sequential pattern mining in large sequence databases

    Unil Yun;John J. Leggett

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

    Unil Yun

  • An efficient algorithm for mining high utility patterns from incremental databases with one database scan

    Unil Yun;Heungmo Ryang;Gangin Lee;Hamido Fujita

  • Mining of high average-utility itemsets using novel list structure and pruning strategy

    Unil Yun;Donggyu Kim

  • Mining maximal frequent patterns by considering weight conditions over data streams

    Unil Yun;Gangin Lee;Keun Ho Ryu

  • Incremental high utility pattern mining with static and dynamic databases

    Unil Yun;Heungmo Ryang

  • Incremental mining of weighted maximal frequent itemsets from dynamic databases

    Unil Yun;Gangin Lee

  • Efficient High Utility Pattern Mining for Establishing Manufacturing Plans With Sliding Window Control

    Unil Yun;Gangin Lee;Eunchul Yoon

  • A new efficient approach for mining uncertain frequent patterns using minimum data structure without false positives

    Gangin Lee;Unil Yun

  • A fast perturbation algorithm using tree structure for privacy preserving utility mining

    Unil Yun;Jiwon Kim

  • Mining lossless closed frequent patterns with weight constraints

    Unil Yun

  • A predictive GA-based model for closed high-utility itemset mining

    Jerry Chun-Wei Lin;Jerry Chun-Wei Lin;Youcef Djenouri;Gautam Srivastava;Gautam Srivastava;Unil Yun

Frequent Co-Authors

Keun Ho Ryu
Keun Ho Ryu Ton Duc Thang University
Bay Vo
Bay Vo Ho Chi Minh City University of Technology
Philippe Fournier-Viger
Philippe Fournier-Viger Shenzhen University
Gautam Srivastava
Gautam Srivastava Brandon University
Hamido Fujita
Hamido Fujita University of Technology Malaysia
Witold Pedrycz
Witold Pedrycz University of Alberta
Youcef Djenouri
Youcef Djenouri University of South-Eastern Norway
Tzung-Pei Hong
Tzung-Pei Hong National University of Kaohsiung
Wensheng Gan
Wensheng Gan Jinan University
Stephen S. Yau
Stephen S. Yau Arizona State University

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