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D-Index & Metrics

Computer Science

D-Index
44
Citations
5851
World Ranking
7709
National Ranking
76

Unil Yun publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Unil Yun sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 206 publications — 48th percentile

48% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Unil Yun D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Unil Yun sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 44 D-Index — 48th percentile

48% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

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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