Setsuo Arikawa

2.9k total citations
68 papers, 786 citations indexed

About

Setsuo Arikawa is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Hardware and Architecture. According to data from OpenAlex, Setsuo Arikawa has authored 68 papers receiving a total of 786 indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Artificial Intelligence, 27 papers in Computational Theory and Mathematics and 12 papers in Hardware and Architecture. Recurrent topics in Setsuo Arikawa's work include Algorithms and Data Compression (37 papers), semigroups and automata theory (20 papers) and Machine Learning and Algorithms (16 papers). Setsuo Arikawa is often cited by papers focused on Algorithms and Data Compression (37 papers), semigroups and automata theory (20 papers) and Machine Learning and Algorithms (16 papers). Setsuo Arikawa collaborates with scholars based in Japan, United States and Chile. Setsuo Arikawa's co-authors include Hiroki Arimura, Ayumi Shinohara, Shinji Kawasoe, Hiroshi Sakamoto, Tatsuya Asai, Kenji Abe, Masayuki Takeda, Takuya Kida, Takeshi Shinohara and Akihiro Yamamoto and has published in prestigious journals such as Theoretical Computer Science, Lecture notes in computer science and Discrete Applied Mathematics.

In The Last Decade

Setsuo Arikawa

63 papers receiving 720 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Setsuo Arikawa Japan 14 546 323 285 210 168 68 786
Philip Bille Denmark 11 462 0.8× 176 0.5× 137 0.5× 136 0.6× 111 0.7× 45 678
Rossano Venturini Italy 16 627 1.1× 236 0.7× 63 0.2× 244 1.2× 167 1.0× 58 787
Rahul Shah United States 13 428 0.8× 124 0.4× 96 0.3× 316 1.5× 380 2.3× 53 692
Giorgio Levi Italy 17 861 1.6× 101 0.3× 666 2.3× 139 0.7× 49 0.3× 83 1.1k
Rudolf Bayer Germany 13 362 0.7× 158 0.5× 90 0.3× 569 2.7× 293 1.7× 28 834
Adam L. Buchsbaum United States 14 333 0.6× 59 0.2× 202 0.7× 296 1.4× 96 0.6× 41 623
Helmut Seidl Germany 17 725 1.3× 125 0.4× 628 2.2× 208 1.0× 68 0.4× 92 978
Paul F. Dietz United States 9 301 0.6× 44 0.1× 134 0.5× 253 1.2× 149 0.9× 16 471
Juha Kärkkäinen Finland 14 549 1.0× 99 0.3× 136 0.5× 122 0.6× 110 0.7× 49 674
Stephen Alstrup Denmark 14 281 0.5× 34 0.1× 300 1.1× 194 0.9× 104 0.6× 43 604

Countries citing papers authored by Setsuo Arikawa

Since Specialization
Citations

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

Fields of papers citing papers by Setsuo Arikawa

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Setsuo Arikawa

This figure shows the co-authorship network connecting the top 25 collaborators of Setsuo Arikawa. A scholar is included among the top collaborators of Setsuo Arikawa 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 Setsuo Arikawa. Setsuo Arikawa 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.
Inenaga, Shunsuke, Ayumi Shinohara, Masayuki Takeda, et al.. (2004). On-line construction of compact directed acyclic word graphs. Discrete Applied Mathematics. 146(2). 156–179. 21 indexed citations
2.
Hirata, Kouichi, et al.. (2003). Criteria for inductive inference with mind changes and anomalies of recursive real-valued functions. IEICE Transactions on Information and Systems. 86(2). 219–227. 2 indexed citations
3.
Kida, Takuya, Tetsuya Matsumoto, Yusuke Shibata, et al.. (2003). Collage system: a unifying framework for compressed pattern matching. Theoretical Computer Science. 298(1). 253–272. 39 indexed citations
4.
Abe, K., et al.. (2002). Optimized Substructure Discovery for Semi-structured Data. Kyushu University Institutional Repository (QIR) (Kyushu University). 1 indexed citations
5.
Baba, Kensuke, Ayumi Shinohara, Masayuki Takeda, Shunsuke Inenaga, & Setsuo Arikawa. (2002). A Note on Randomized Algorithm for String Matching with Mismatches. Kyushu University Institutional Repository (QIR) (Kyushu University). 10(1). 2–12. 4 indexed citations
6.
Arikawa, Setsuo & Ayumi Shinohara. (2002). Progress in Discovery Science, Final Report of the Japanese Discovery Science Project. 5 indexed citations
7.
Arimura, Hiroki, et al.. (2001). Efficient Substring Traversal with Suffix Arrays. Kyushu University Institutional Repository (QIR) (Kyushu University). 2 indexed citations
8.
Takeda, Masayuki, Yusuke Shibata, Tetsuya Matsumoto, et al.. (2001). Speeding Up String Pattern Matching by Text Compression: The Dawn of a New Era. 42(3). 370–384. 10 indexed citations
9.
Arimura, Hiroki, et al.. (2001). Efficient Discovery of Proximity Patterns with Suffix Arrays. 152–156. 4 indexed citations
10.
Inenaga, Shunsuke, et al.. (2001). Construction of the CDAWG for a Trie.. 37–48. 7 indexed citations
11.
Arikawa, Setsuo, et al.. (2001). A comparison of identification criteria for inductive inference of recursive real-valued functions. Theoretical Computer Science. 268(2). 351–366. 2 indexed citations
12.
Asai, Tatsuya, et al.. (2001). Efficient Substructure Discovery from Large Semi-Structured Data. IEICE Transactions on Information and Systems. 101(12). 1–2763. 18 indexed citations
13.
Matsumoto, Tsubasa, et al.. (2000). Compressed Pattern Matching for SEQUITUR. Kyushu University Institutional Repository (QIR) (Kyushu University). 1 indexed citations
14.
Arimura, Hiroki, et al.. (1998). A Fast Algorithm for Discovering Optimal String Patterns in Large Text Databases. Kyushu University Institutional Repository (QIR) (Kyushu University).
15.
Kasai, Toru, et al.. (1998). Text Data Mining Based on Optimal Pattern Discovery : Towards a Scalable Data Mining System for Large Text Databases. IPSJ SIG Notes. 98(57). 151–156. 3 indexed citations
16.
Arimura, Hiroki, et al.. (1994). Protein Motif Discovery from Positive Examples by Minimal Multiple Generalization over Regular Patterns. Proceedings Genome Informatics Workshop/Genome informatics. 5. 39–48. 2 indexed citations
17.
Shinohara, Ayumi, et al.. (1993). Running Learning Systems in Parallel for Machine Discovery from Sequences. Proceedings Genome Informatics Workshop/Genome informatics. 4. 74–83. 2 indexed citations
18.
Miyano, Satoru, et al.. (1992). Knowledge Acquisition from Amino Acid Sequences by Decision Trees and Indexing. Proceedings Genome Informatics Workshop/Genome informatics. 3. 69–72. 3 indexed citations
19.
Arikawa, Setsuo, Takeshi Shinohara, & Akihiro Yamamoto. (1992). Learning elementary formal system. Theoretical Computer Science. 95(1). 97–113. 35 indexed citations
20.
Arikawa, Setsuo, Takeshi Shinohara, & Akihiro Yamamoto. (1989). Elementary formal system as a unifying framework for language learning. Conference on Learning Theory. 312–327. 9 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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