Cheul Hwang

700 total citations
11 papers, 523 citations indexed

About

Cheul Hwang is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Management Science and Operations Research. According to data from OpenAlex, Cheul Hwang has authored 11 papers receiving a total of 523 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 4 papers in Computer Vision and Pattern Recognition and 3 papers in Management Science and Operations Research. Recurrent topics in Cheul Hwang's work include Fuzzy Logic and Control Systems (6 papers), Image Retrieval and Classification Techniques (4 papers) and Multi-Criteria Decision Making (3 papers). Cheul Hwang is often cited by papers focused on Fuzzy Logic and Control Systems (6 papers), Image Retrieval and Classification Techniques (4 papers) and Multi-Criteria Decision Making (3 papers). Cheul Hwang collaborates with scholars based in South Korea, United States and Malaysia. Cheul Hwang's co-authors include Frank Chung-Hoon Rhee, Hichem Frigui and Ji Sun Shin and has published in prestigious journals such as Pattern Recognition, IEEE Transactions on Fuzzy Systems and The Royal Society of Chemistry’s Journals, Books and Databases (The Royal Society of Chemistry).

In The Last Decade

Cheul Hwang

10 papers receiving 498 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Cheul Hwang South Korea	6	378	147	111	98	95	11	523
Yingkang Hu United States	8	191 0.5×	170 1.2×	54 0.5×	25 0.3×	39 0.4×	19	392
Marzieh Zarinbal Iran	9	208 0.6×	178 1.2×	33 0.3×	66 0.7×	23 0.2×	25	445
K. Chidananda Gowda India	8	436 1.2×	224 1.5×	44 0.4×	28 0.3×	72 0.8×	15	656
Ken Nozaki Japan	5	727 1.9×	61 0.4×	140 1.3×	128 1.3×	205 2.2×	9	863
Hanuman Verma India	9	172 0.5×	199 1.4×	18 0.2×	57 0.6×	32 0.3×	13	426
Masaru Shimbo Japan	11	298 0.8×	71 0.5×	77 0.7×	148 1.5×	157 1.7×	46	466
Can Gao China	16	394 1.0×	262 1.8×	30 0.3×	117 1.2×	344 3.6×	65	748
Daniel Graves Canada	8	298 0.8×	171 1.2×	14 0.1×	27 0.3×	47 0.5×	17	428
Manabu Ichino Japan	7	194 0.5×	95 0.6×	39 0.4×	18 0.2×	62 0.7×	24	353
Ming-Hu Ha China	10	147 0.4×	59 0.4×	117 1.1×	161 1.6×	105 1.1×	69	413

Countries citing papers authored by Cheul Hwang

Since Specialization

Citations

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

Fields of papers citing papers by Cheul Hwang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Cheul Hwang

This figure shows the co-authorship network connecting the top 25 collaborators of Cheul Hwang. A scholar is included among the top collaborators of Cheul Hwang 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 Cheul Hwang. Cheul Hwang 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:

11 of 11 papers shown

1.

Frigui, Hichem & Cheul Hwang. (2008). Semi-supervised Clustering and Aggregation of Relational Data. 590–595. 1 indexed citations

2.

Frigui, Hichem & Cheul Hwang. (2008). Fuzzy Clustering and Aggregation of Relational Data With Instance-Level Constraints. IEEE Transactions on Fuzzy Systems. 16(6). 1565–1581. 24 indexed citations

3.

Frigui, Hichem & Cheul Hwang. (2007). Adaptive Concept Learning through Clustering and Aggregation of Relational Data. 90–101. 3 indexed citations

4.

Hwang, Cheul & Frank Chung-Hoon Rhee. (2007). Uncertain Fuzzy Clustering: Interval Type-2 Fuzzy Approach to $C$-Means. IEEE Transactions on Fuzzy Systems. 15(1). 107–120. 328 indexed citations

5.

Frigui, Hichem, et al.. (2007). Clustering and aggregation of relational data with applications to image database categorization. Pattern Recognition. 40(11). 3053–3068. 1 indexed citations

6.

Shin, Ji Sun, et al.. (2006). Hierarchical Land-Use Classification Using Optical Imagery and LiDAR Data. 2. 2746–2749. 1 indexed citations

7.

Hwang, Cheul & Frank Chung-Hoon Rhee. (2005). An interval type-2 fuzzy C spherical shells algorithm. 2. 1117–1122. 23 indexed citations

8.

Hwang, Cheul & Frank Chung-Hoon Rhee. (2004). Interval Type-2 Fuzzy C Clustering for Detecting Spherical Shells. Journal of Korean institute of intelligent systems. 14(6). 713–719.

9.

Rhee, Frank Chung-Hoon & Cheul Hwang. (2004). An interval type-2 fuzzy K-nearest neighbor. 2. 802–807. 39 indexed citations

10.

Rhee, Frank Chung-Hoon & Cheul Hwang. (2003). An interval type-2 fuzzy perceptron. The Royal Society of Chemistry’s Journals, Books and Databases (The Royal Society of Chemistry). 2. 1331–1335. 20 indexed citations

11.

Rhee, Frank Chung-Hoon & Cheul Hwang. (2002). A type-2 fuzzy C-means clustering algorithm. 4. 1926–1929. 83 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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