Gangin Lee

1.1k total citations
29 papers, 880 citations indexed

About

Gangin Lee is a scholar working on Information Systems, Computational Theory and Mathematics and Artificial Intelligence. According to data from OpenAlex, Gangin Lee has authored 29 papers receiving a total of 880 indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Information Systems, 23 papers in Computational Theory and Mathematics and 19 papers in Artificial Intelligence. Recurrent topics in Gangin Lee's work include Data Mining Algorithms and Applications (27 papers), Rough Sets and Fuzzy Logic (23 papers) and Imbalanced Data Classification Techniques (12 papers). Gangin Lee is often cited by papers focused on Data Mining Algorithms and Applications (27 papers), Rough Sets and Fuzzy Logic (23 papers) and Imbalanced Data Classification Techniques (12 papers). Gangin Lee collaborates with scholars based in South Korea, United States and Norway. Gangin Lee's co-authors include Unil Yun, Heungmo Ryang, Eunchul Yoon, Keun Ho Ryu, Hamido Fujita, Kyungmin Lee, Dong-Gyu Kim, Jiwon Kim, Chulhong Kim and Heonho Kim and has published in prestigious journals such as IEEE Transactions on Industrial Electronics, Expert Systems with Applications and Information Sciences.

In The Last Decade

Gangin Lee

28 papers receiving 859 citations

Peers

Gangin Lee

CTM

CNC

Heungmo Ryang South Korea

Tin Truong Vietnam

Guo-Cheng Lan Taiwan

Bai En Shie Taiwan

Karam Gouda Egypt

Hua-Fu Li Taiwan

Nicolas Pasquier France

Manuel Calimlim United States

Doug Burdick United States

Lotfi Lakhal France

Heungmo Ryang South Korea

Gangin Lee

724 ×0.9

470 ×0.8

539 ×1.0

CTM

238 ×0.9

56 ×0.6

CNC

Citations per year, relative to Gangin Lee Gangin Lee (= 1×) peers Heungmo Ryang

Countries citing papers authored by Gangin Lee

Since Specialization

Citations

This map shows the geographic impact of Gangin Lee's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Gangin Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Gangin Lee more than expected).

Fields of papers citing papers by Gangin Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Gangin Lee. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Gangin Lee. The network helps show where Gangin Lee may publish in the future.

Co-authorship network of co-authors of Gangin Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Gangin Lee. A scholar is included among the top collaborators of Gangin Lee based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Gangin Lee. Gangin Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Yun, Unil, Heonho Kim, Gangin Lee, et al.. (2020). Erasable pattern mining based on tree structures with damped window over data streams. Engineering Applications of Artificial Intelligence. 94. 103735–103735. 19 indexed citations

Yun, Unil, Gangin Lee, & Eunchul Yoon. (2019). Advanced approach of sliding window based erasable pattern mining with list structure of industrial fields. Information Sciences. 494. 37–59. 38 indexed citations

Yun, Unil, et al.. (2019). Efficient approach for incremental high utility pattern mining with indexed list structure. Future Generation Computer Systems. 95. 221–239. 54 indexed citations

Lee, Gangin & Unil Yun. (2017). Single-pass based efficient erasable pattern mining using list data structure on dynamic incremental databases. Future Generation Computer Systems. 80. 12–28. 34 indexed citations

Lee, Gangin, Unil Yun, & Keun Ho Ryu. (2017). Mining Frequent Weighted Itemsets without Storing Transaction IDs and Generating Candidates. International Journal of Uncertainty Fuzziness and Knowledge-Based Systems. 25(1). 111–144. 28 indexed citations

Yun, Unil, Heungmo Ryang, Gangin Lee, & Hamido Fujita. (2017). An efficient algorithm for mining high utility patterns from incremental databases with one database scan. Knowledge-Based Systems. 124. 188–206. 77 indexed citations

Lee, Gangin & Unil Yun. (2017). Performance and characteristic analysis of maximal frequent pattern mining methods using additional factors. Soft Computing. 22(13). 4267–4273. 8 indexed citations

Yun, Unil, Gangin Lee, & Eunchul Yoon. (2017). Efficient High Utility Pattern Mining for Establishing Manufacturing Plans With Sliding Window Control. IEEE Transactions on Industrial Electronics. 64(9). 7239–7249. 50 indexed citations

Yun, Unil & Gangin Lee. (2016). Incremental mining of weighted maximal frequent itemsets from dynamic databases. Expert Systems with Applications. 54. 304–327. 49 indexed citations

10.

Yun, Unil, Gangin Lee, & Chulhong Kim. (2016). The Smallest Valid Extension-Based Efficient, Rare Graph Pattern Mining, Considering Length-Decreasing Support Constraints and Symmetry Characteristics of Graphs. Symmetry. 8(5). 32–32. 8 indexed citations

11.

Lee, Gangin & Unil Yun. (2016). A new efficient approach for mining uncertain frequent patterns using minimum data structure without false positives. Future Generation Computer Systems. 68. 89–110. 62 indexed citations

12.

Lee, Gangin, Unil Yun, Heungmo Ryang, & Dong-Gyu Kim. (2016). Approximate Maximal Frequent Pattern Mining with Weight Conditions and Error Tolerance. International Journal of Pattern Recognition and Artificial Intelligence. 30(6). 1650012–1650012. 22 indexed citations

13.

Lee, Gangin, et al.. (2016). A Weight-Based Approach: Frequent Graph Pattern Mining with Length-Decreasing Support Constraints Using Weighted Smallest Valid Extension. Advanced Science Letters. 22(9). 2480–2484. 2 indexed citations

14.

Yun, Unil, et al.. (2016). Mining recent high average utility patterns based on sliding window from stream data. Journal of Intelligent & Fuzzy Systems. 30(6). 3605–3617. 30 indexed citations

15.

Lee, Gangin, Unil Yun, & Heungmo Ryang. (2015). Mining weighted erasable patterns by using underestimated constraint-based pruning technique. Journal of Intelligent & Fuzzy Systems. 28(3). 1145–1157. 38 indexed citations

16.

Lee, Gangin, et al.. (2015). Multiple Minimum Support-Based Rare Graph Pattern Mining Considering Symmetry Feature-Based Growth Technique and the Differing Importance of Graph Elements. Symmetry. 7(3). 1151–1163. 10 indexed citations

17.

Lee, Gangin & Unil Yun. (2015). Analysis and Performance Evaluation of Pattern Condensing Techniques used in Representative Pattern Mining. Journal of Internet Computing and services. 16(2). 77–83. 4 indexed citations

18.

Yun, Unil & Gangin Lee. (2014). A Weighted Frequent Graph Pattern Mining Approach considering Length-Decreasing Support Constraints. Journal of Internet Computing and services. 15(6). 125–132. 2 indexed citations

19.

Yun, Unil, Gangin Lee, & Keun Ho Ryu. (2013). Mining maximal frequent patterns by considering weight conditions over data streams. Knowledge-Based Systems. 55. 49–65. 59 indexed citations

20.

Kim, Jiwon, Unil Yun, Heungmo Ryang, et al.. (2013). A blog ranking algorithm using analysis of both blog influence and characteristics of blog posts. Cluster Computing. 18(1). 157–164. 12 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact