Shyue-Liang Wang

1.8k total citations
97 papers, 1.2k citations indexed

About

Shyue-Liang Wang is a scholar working on Artificial Intelligence, Information Systems and Computational Theory and Mathematics. According to data from OpenAlex, Shyue-Liang Wang has authored 97 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 68 papers in Artificial Intelligence, 65 papers in Information Systems and 45 papers in Computational Theory and Mathematics. Recurrent topics in Shyue-Liang Wang's work include Data Mining Algorithms and Applications (59 papers), Rough Sets and Fuzzy Logic (42 papers) and Privacy-Preserving Technologies in Data (31 papers). Shyue-Liang Wang is often cited by papers focused on Data Mining Algorithms and Applications (59 papers), Rough Sets and Fuzzy Logic (42 papers) and Privacy-Preserving Technologies in Data (31 papers). Shyue-Liang Wang collaborates with scholars based in Taiwan, United States and China. Shyue-Liang Wang's co-authors include Tzung‐Pei Hong, Jerry Chun‐Wei Lin, Guo-Cheng Lan, Vincent S. Tseng, I‐Hsien Ting, Yu‐Chuan Tsai, Hung‐Yu Kao, Yu-Lung Wu, Wensheng Gan and Han‐Chieh Chao and has published in prestigious journals such as Expert Systems with Applications, Information Sciences and Fuzzy Sets and Systems.

In The Last Decade

Shyue-Liang Wang

87 papers receiving 1.1k citations

Peers

Shyue-Liang Wang
Syed Khairuzzaman Tanbeer South Korea
Loan T. T. Nguyen Vietnam
Gholamreza Nakhaeizadeh Germany
Petko Valtchev Canada
Ch. Aswani Kumar India
Mika Klemettinen Finland
Engelbert Mephu Nguifo France
Giuseppe Psaila Italy
Syed Khairuzzaman Tanbeer South Korea
Shyue-Liang Wang
Citations per year, relative to Shyue-Liang Wang Shyue-Liang Wang (= 1×) peers Syed Khairuzzaman Tanbeer

Countries citing papers authored by Shyue-Liang Wang

Since Specialization
Citations

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

Fields of papers citing papers by Shyue-Liang Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shyue-Liang Wang. 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 Shyue-Liang Wang. The network helps show where Shyue-Liang Wang may publish in the future.

Co-authorship network of co-authors of Shyue-Liang Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Shyue-Liang Wang. A scholar is included among the top collaborators of Shyue-Liang Wang 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 Shyue-Liang Wang. Shyue-Liang Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Hong, Tzung‐Pei, et al.. (2022). A Multi-Scale Convolutional Neural Network for Rotation-Invariant Recognition. Electronics. 11(4). 661–661. 3 indexed citations
2.
Hong, Tzung‐Pei, et al.. (2018). Maintenance of Erasable Itemsets for Product Deletion. 1–4. 1 indexed citations
3.
Hong, Tzung‐Pei, et al.. (2017). Mining high utility partial periodic pattern by GPA. 820–824.
4.
5.
Lan, Guo-Cheng, et al.. (2014). Fast discovery of high fuzzy utility itemsets. 2764–2767. 3 indexed citations
6.
Wang, Shyue-Liang, et al.. (2014). K-anonymous path privacy on social graphs. Journal of Intelligent & Fuzzy Systems. 26(3). 1191–1199. 1 indexed citations
7.
Lin, Jerry Chun‐Wei, et al.. (2013). A Greedy-based Approach for Hiding Sensitive Itemsets by Transaction Insertion. J. Inf. Hiding Multim. Signal Process.. 4. 201–214. 30 indexed citations
8.
Lan, Guo-Cheng, et al.. (2013). Enhancing the Efficiency in Mining Weighted Frequent Itemsets. 1104–1108. 5 indexed citations
9.
Hong, Tzung‐Pei, et al.. (2011). Mining a Complete Set of Fuzzy Multiple-Level Rules. International Journal of Fuzzy Systems. 13(2). 111–119. 2 indexed citations
10.
Hong, Tzung‐Pei, et al.. (2011). A heuristic data-sanitization approach based on TF-IDF. 156–164. 8 indexed citations
11.
Wang, Shyue-Liang, et al.. (2011). Extending suppression for anonymization on set-valued data. 7(12). 6849–6863. 5 indexed citations
12.
Hong, Tzung‐Pei, et al.. (2010). Evolutionary privacy-preserving data mining. World Automation Congress. 1–7. 3 indexed citations
13.
Hong, Tzung‐Pei, et al.. (2008). Extracting membership functions in fuzzy data mining by Ant Colony Systems. 34. 3979–3984. 5 indexed citations
14.
Hong, Tzung‐Pei, et al.. (2007). Learning cross-level certain and possible rules by rough sets. Expert Systems with Applications. 34(3). 1698–1706. 31 indexed citations
15.
Hong, Tzung‐Pei, et al.. (2007). An improved data mining approach using predictive itemsets. Expert Systems with Applications. 36(1). 72–80. 10 indexed citations
16.
Hong, Tzung‐Pei, et al.. (2006). Fuzzy Weighted Data Mining from Quantitative Transactions with Linguistic Minimum Supports and Confidences. International Journal of Fuzzy Systems. 8(4). 173–182. 7 indexed citations
17.
Wang, Shyue-Liang, et al.. (2006). One Scan Sanitization of Informative Association Rules. 8. 516–521. 1 indexed citations
18.
Hong, Tzung‐Pei, et al.. (2001). A Fuzzy AprioriTid Algorithm With Reduced Computational Time.. 360–363.
19.
Hong, Tzung‐Pei, et al.. (2001). A Fuzzy CDS-based Scheduling Algorithm for More Than Two Machine Centers. Journal of Advanced Computational Intelligence and Intelligent Informatics. 5(4). 239–245.
20.
Hong, Tzung‐Pei, et al.. (2000). Knowledge acquisition from quantitative data using the rough-set theory. Intelligent Data Analysis. 4(3-4). 289–304. 20 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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