Shusaku Tsumoto

4.8k total citations
232 papers, 1.8k citations indexed

About

Shusaku Tsumoto is a scholar working on Computational Theory and Mathematics, Information Systems and Artificial Intelligence. According to data from OpenAlex, Shusaku Tsumoto has authored 232 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 127 papers in Computational Theory and Mathematics, 113 papers in Information Systems and 103 papers in Artificial Intelligence. Recurrent topics in Shusaku Tsumoto's work include Rough Sets and Fuzzy Logic (124 papers), Data Mining Algorithms and Applications (105 papers) and Data Management and Algorithms (44 papers). Shusaku Tsumoto is often cited by papers focused on Rough Sets and Fuzzy Logic (124 papers), Data Mining Algorithms and Applications (105 papers) and Data Management and Algorithms (44 papers). Shusaku Tsumoto collaborates with scholars based in Japan, United States and Canada. Shusaku Tsumoto's co-authors include Shoji Hirano, S. Hirano, Tsau Young Lin, Hiroshi Tanaka, Lech Polkowski, Yuko Tsumoto, Zbigniew W. Raś, Mohand-Saïd Hacid, Takahira Yamaguchi and Miho Ohsaki and has published in prestigious journals such as Expert Systems with Applications, Information Sciences and The Journal of Clinical Psychiatry.

In The Last Decade

Shusaku Tsumoto

207 papers receiving 1.7k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shusaku Tsumoto Japan 21 940 846 752 349 232 232 1.8k
Roman W. Świniarski United States 11 817 0.9× 740 0.9× 613 0.8× 173 0.5× 188 0.8× 29 1.5k
Bay Vo Vietnam 29 1.2k 1.3× 1.4k 1.7× 1.8k 2.4× 560 1.6× 120 0.5× 114 2.5k
Daniel Sánchez Spain 21 702 0.7× 1.0k 1.2× 775 1.0× 429 1.2× 237 1.0× 123 1.8k
Aswani Kumar Cherukuri India 20 602 0.6× 871 1.0× 369 0.5× 333 1.0× 237 1.0× 86 1.6k
José A. Gámez Spain 25 288 0.3× 1.2k 1.4× 285 0.4× 230 0.7× 292 1.3× 130 2.1k
Jianying Hu United States 18 269 0.3× 695 0.8× 340 0.5× 166 0.5× 110 0.5× 47 1.5k
Dominik Ślȩzak Poland 22 999 1.1× 814 1.0× 743 1.0× 259 0.7× 375 1.6× 136 1.8k
Lin Sun China 27 1.5k 1.6× 1.5k 1.7× 706 0.9× 111 0.3× 211 0.9× 108 2.8k
Chuan Luo China 31 2.0k 2.1× 1.4k 1.6× 1.3k 1.7× 337 1.0× 501 2.2× 97 2.8k
Tomoharu Nakashima Japan 19 444 0.5× 1.8k 2.1× 301 0.4× 80 0.2× 292 1.3× 122 2.3k

Countries citing papers authored by Shusaku Tsumoto

Since Specialization
Citations

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

Fields of papers citing papers by Shusaku Tsumoto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shusaku Tsumoto

This figure shows the co-authorship network connecting the top 25 collaborators of Shusaku Tsumoto. A scholar is included among the top collaborators of Shusaku Tsumoto 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 Shusaku Tsumoto. Shusaku Tsumoto 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.
Tsumoto, Shusaku, et al.. (2022). Expectation–Maximization (EM) Clustering as a Preprocessing Method for Clinical Pathway Mining. RePEc: Research Papers in Economics. 16(1). 25–52. 3 indexed citations
2.
Tsumoto, Shusaku, et al.. (2021). Determination of Disease from Discharge Summaries. RePEc: Research Papers in Economics. 15(1). 49–66. 1 indexed citations
3.
Tsumoto, Shusaku, et al.. (2021). Mining Clinical Pathways Using Dual Clustering. RePEc: Research Papers in Economics. 15(2). 287–307. 2 indexed citations
4.
Tsumoto, Shusaku, et al.. (2021). Order Trajectory Analysis for Monitoring Clinical Process. RePEc: Research Papers in Economics. 16(1). 53–70. 1 indexed citations
5.
Satoh, Ken, et al.. (2018). Argumentation with Goals for Clinical Decision Support in Multimorbidity. Open Repository and Bibliography (University of Luxembourg). 2031–2033. 5 indexed citations
6.
Tsumoto, Shusaku, et al.. (2014). Agenda: SRII Health Care Analytics + Hackathon Day (Thursday, April 24, 2014). xxxii–xxxii. 1 indexed citations
7.
Hirano, Shoji & Shusaku Tsumoto. (2010). Hierarchical, Granular Representation of Non-metric Proximity Data. 26. 173–173. 1 indexed citations
8.
Tsumoto, Shusaku & Shoji Hirano. (2009). Dependency and Granularity inData.. 1864–1872. 4 indexed citations
9.
Ohsawa, Yukio & Shusaku Tsumoto. (2006). Chance Discoveries in Real World Decision Making: Data-based Interaction of Human intelligence and Artificial Intelligence (Studies in Computational Intelligence). Springer eBooks. 4 indexed citations
10.
Tsumoto, Shusaku, Takahira Yamaguchi, Masayuki Numao, & Hiroshi Motoda. (2005). Active Mining: Second International Workshop, AM 2003, Maebashi, Japan, October 28, 2003, Revised Selected Papers (Lecture Notes in Computer Science / Lecture Notes in Artificial Intelligence). Springer eBooks. 4 indexed citations
11.
Lin, Tsau Young, Setsuo Ohsuga, Churn‐Jung Liau, Xiaohua Hu, & Shusaku Tsumoto. (2005). Foundations of Data Mining and Knowledge Discovery (Studies in Computational Intelligence) (Studies in Computational Intelligence). Springer eBooks. 1 indexed citations
12.
Tsumoto, Shusaku & Shoji Hirano. (2005). Automated discovery of chronological patterns in long time-series medical datasets: Research Articles. International Journal of Intelligent Systems. 20(7). 737–757. 2 indexed citations
13.
Tsumoto, Shusaku. (2004). Extraction of structure of medical diagnosis from clinical data. 59(2). 271–285. 1 indexed citations
14.
Hirano, Shoji & Shusaku Tsumoto. (2003). Dealing with relative similarity in clustering: an indiscernibility based approach. Knowledge Discovery and Data Mining. 513–518. 1 indexed citations
15.
Yokoyama, Shigeki, Shusaku Tsumoto, Takeshi Yamakawa, et al.. (2001). Study on the Association between the Patients' Clinical Background and the Anaerobes by Data Mining In Infectious Diseases Database. 7(1). 69–75. 4 indexed citations
16.
Polkowski, Lech, Shusaku Tsumoto, & Tsau Young Lin. (2000). Rough set methods and applications: new developments in knowledge discovery in information systems. 123 indexed citations
17.
Tsumoto, Shusaku & Hiroshi Tanaka. (1995). Automated discovery of functional components of proteins from amino-acid sequences based on rough sets and change of representation. Knowledge Discovery and Data Mining. 318–324. 3 indexed citations
18.
Tsumoto, Shusaku & Hiroshi Tanaka. (1995). Automated selection of rule induction methods based on recursive iteration of resampling methods and multiple statistical testing. Knowledge Discovery and Data Mining. 312–317. 1 indexed citations
19.
Tsumoto, Shusaku & Hiroshi Tanaka. (1994). Selection of probabilistic measure estimation method based on recursive iteration of resampling methods. Knowledge Discovery and Data Mining. 121–132. 1 indexed citations
20.
Tsumoto, Shusaku, Hiroshi Tanaka, Kouhei Tsumoto, & Iori Kumagai. (1994). Comparative Analysis of Amino Acid Sequences based on Rough Sets and Domain Knowledge Hierarchy. Proceedings Genome Informatics Workshop/Genome informatics. 5. 59–69.

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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