Heonho Kim

515 total citations
21 papers, 397 citations indexed

About

Heonho Kim is a scholar working on Information Systems, Artificial Intelligence and Computational Theory and Mathematics. According to data from OpenAlex, Heonho Kim has authored 21 papers receiving a total of 397 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Information Systems, 15 papers in Artificial Intelligence and 14 papers in Computational Theory and Mathematics. Recurrent topics in Heonho Kim's work include Data Mining Algorithms and Applications (18 papers), Rough Sets and Fuzzy Logic (13 papers) and Imbalanced Data Classification Techniques (9 papers). Heonho Kim is often cited by papers focused on Data Mining Algorithms and Applications (18 papers), Rough Sets and Fuzzy Logic (13 papers) and Imbalanced Data Classification Techniques (9 papers). Heonho Kim collaborates with scholars based in South Korea, Vietnam and Norway. Heonho Kim's co-authors include Unil Yun, Jerry Chun‐Wei Lin, Bay Vo, Bay Vo, Witold Pedrycz, Eunchul Yoon, Chanhee Lee, Tin Truong, Hyunsoo Kim and Philippe Fournier‐Viger and has published in prestigious journals such as Expert Systems with Applications, Information Sciences and IEEE Transactions on Cybernetics.

In The Last Decade

Heonho Kim

19 papers receiving 390 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Heonho Kim South Korea	14	353	249	236	105	49	21	397
Ted Gueniche Canada	2	244 0.7×	156 0.6×	136 0.6×	81 0.8×	43 0.9×	2	305
Antonio Gomariz Spain	3	219 0.6×	143 0.6×	117 0.5×	70 0.7×	42 0.9×	4	281
Yu-Chiang Li Taiwan	9	238 0.7×	184 0.7×	163 0.7×	97 0.9×	20 0.4×	15	424
Zhigang Zheng Australia	7	347 1.0×	181 0.7×	234 1.0×	131 1.2×	68 1.4×	7	385
Jieh-Shan Yeh Taiwan	7	269 0.8×	168 0.7×	165 0.7×	91 0.9×	20 0.4×	13	334
Hai Duong Vietnam	10	299 0.8×	174 0.7×	215 0.9×	97 0.9×	40 0.8×	27	316
Quang-Huy Duong Norway	7	271 0.8×	163 0.7×	208 0.9×	99 0.9×	20 0.4×	12	299
Joong Hyuk Chang South Korea	11	505 1.4×	347 1.4×	256 1.1×	262 2.5×	163 3.3×	20	598
Ferenc Bodon Hungary	5	215 0.6×	143 0.6×	133 0.6×	88 0.8×	38 0.8×	6	273
Robert J. Hilderman Canada	10	203 0.6×	154 0.6×	103 0.4×	101 1.0×	45 0.9×	26	286

Countries citing papers authored by Heonho Kim

Since Specialization

Citations

This map shows the geographic impact of Heonho Kim'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 Heonho Kim with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Heonho Kim more than expected).

Fields of papers citing papers by Heonho Kim

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Heonho Kim. 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 Heonho Kim. The network helps show where Heonho Kim may publish in the future.

Co-authorship network of co-authors of Heonho Kim

This figure shows the co-authorship network connecting the top 25 collaborators of Heonho Kim. A scholar is included among the top collaborators of Heonho Kim 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 Heonho Kim. Heonho Kim 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

Kim, Do‐Young, et al.. (2025). Pre-Eminent Utility Driven Data Analytics Based on Prelarge Patterns for Dynamic Transaction Deletion in IoT Environments. IEEE Internet of Things Journal. 12(13). 24470–24489.

Kim, Doyoung, et al.. (2025). Efficient mining of incremental high utility patterns with negative unit profits over all the accumulated stream data. Knowledge-Based Systems. 325. 113956–113956.

Kim, Hanju, et al.. (2025). DFPM: Damped window-based flexible periodic pattern analysis on the time-decaying model. Future Generation Computer Systems. 174. 107970–107970. 1 indexed citations

Kim, Heonho, et al.. (2024). Regularity-driven pattern extraction and analysis approach by the pre-pruning technique without pattern loss. Future Generation Computer Systems. 166. 107670–107670. 3 indexed citations

Kim, Hanju, Chanhee Lee, Heonho Kim, et al.. (2024). Uncertainty Oriented-Incremental Erasable Pattern Mining Over Data Streams. IEEE Transactions on Systems Man and Cybernetics Systems. 55(2). 1451–1465. 5 indexed citations

Kim, Hanju, Heonho Kim, Bay Vo, et al.. (2024). An efficient approach for incremental erasable utility pattern mining from non-binary data. Knowledge and Information Systems. 66(10). 5919–5958. 6 indexed citations

Kim, Heonho, Sinyoung Kim, Hanju Kim, et al.. (2023). An advanced approach for incremental flexible periodic pattern mining on time-series data. Expert Systems with Applications. 230. 120697–120697. 13 indexed citations

Lee, Chanhee, Heonho Kim, Sinyoung Kim, et al.. (2023). Pre-large based high utility pattern mining for transaction insertions in incremental database. Knowledge-Based Systems. 268. 110478–110478. 27 indexed citations

Kim, Heonho, Chanhee Lee, Tin Truong, et al.. (2022). Mining high occupancy patterns to analyze incremental data in intelligent systems. ISA Transactions. 131. 460–475. 16 indexed citations

10.

Lee, Chanhee, et al.. (2022). Efficient approach of sliding window-based high average-utility pattern mining with list structures. Knowledge-Based Systems. 256. 109702–109702. 28 indexed citations

11.

Kim, Heonho, et al.. (2022). An efficient approach for mining maximized erasable utility patterns. Information Sciences. 609. 1288–1308. 13 indexed citations

12.

Kim, Heonho, Chanhee Lee, Bay Vo, et al.. (2022). Scalable and Efficient Approach for High Temporal Fuzzy Utility Pattern Mining. IEEE Transactions on Cybernetics. 53(12). 7672–7685. 16 indexed citations

13.

Yun, Unil, et al.. (2021). Prelarge-Based Utility-Oriented Data Analytics for Transaction Modifications in Internet of Things. IEEE Internet of Things Journal. 8(24). 17333–17344. 13 indexed citations

14.

Yun, Unil, Heonho Kim, Hyunsoo Kim, et al.. (2021). RHUPS. ACM Transactions on Intelligent Systems and Technology. 12(2). 1–27. 34 indexed citations

15.

Kim, Heonho, et al.. (2020). Efficient list based mining of high average utility patterns with maximum average pruning strategies. Information Sciences. 543. 85–105. 48 indexed citations

16.

Yun, Unil, et al.. (2020). Approximate high utility itemset mining in noisy environments. Knowledge-Based Systems. 212. 106596–106596. 37 indexed citations

17.

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

18.

Kim, Heonho, Unil Yun, Bay Vo, Jerry Chun‐Wei Lin, & Witold Pedrycz. (2020). Periodicity-Oriented Data Analytics on Time-Series Data for Intelligence System. IEEE Systems Journal. 15(4). 4958–4969. 15 indexed citations

19.

Kim, Heonho, et al.. (2020). Damped sliding based utility oriented pattern mining over stream data. Knowledge-Based Systems. 213. 106653–106653. 32 indexed citations

20.

Yun, Unil, Heonho Kim, Eunchul Yoon, et al.. (2019). Efficient transaction deleting approach of pre-large based high utility pattern mining in dynamic databases. Future Generation Computer Systems. 103. 58–78. 46 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.

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