Hiroki Arimura

3.7k citations
79 papers · 1.1k · h-index 15

Impact in

Papers in

Hiroki Arimura

70 papers receiving 991 citations

Peers

Hiroki Arimura
Comparison fields: 5 of 84
  • Signal Processing 323
  • Information Systems 654
  • Computational Theory and Mathematics 419
  • Artificial Intelligence 538
  • Computer Networks and Communications 274
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Jiefeng Cheng Hong Kong
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Citations per field
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Citations per year

Countries citing papers authored by Hiroki Arimura

Since Specialization
Citations

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

Fields of papers citing papers by Hiroki Arimura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Hiroki Arimura, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Hiroki Arimura Line = papers co-authored together Hiroki Arimura links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 79 papers — load more, or switch the sort, to bring in the rest.

#Work
1
LCM ver. 2: Efficient Mining Algorithms for Frequent/Closed/Maximal Itemsets
2004194
2 2002189
3 2005116
4
Efficient Substructure Discovery from Large Semi-structed Data
2001105
5
LCM: An Efficient Algorithm for Enumerating Frequent Closed Item Sets.
200378
6 202047
7 200324
8 200020
9
Efficient Substructure Discovery from Large Semi-Structured Data
200118
10 200017
11 201417
12 199716
13 200616
14 201716
15 200014
16 201014
17 200912
18 201611
19
Extracting Partial Structures from HTML Documents
200110
20
Inductive Inference of Prolog Programs with Linear Data Dependency from Positive Data
199310

About Hiroki Arimura

Hiroki Arimura is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Information Systems, Computer Networks and Communications and Signal Processing, having authored 79 papers that have together received 1.1k indexed citations. Recurring topics across this work include Algorithms and Data Compression (34 papers), Data Mining Algorithms and Applications (22 papers), semigroups and automata theory (13 papers), Machine Learning and Algorithms (13 papers), Data Management and Algorithms (12 papers), Network Packet Processing and Optimization (11 papers), Rough Sets and Fuzzy Logic (11 papers) and Advanced Database Systems and Queries (8 papers). The work is most often cited by research in Signal Processing (323 citations), Information Systems (654 citations), Computational Theory and Mathematics (419 citations), Artificial Intelligence (538 citations) and Computer Networks and Communications (274 citations). Hiroki Arimura has collaborated with scholars based in Japan, United States and Australia. Frequent co-authors include Takeaki Uno, Masashi Kiyomi, Tatsuya Asai, Setsuo Arikawa, Shinji Kawasoe, Hiroshi Sakamoto, Kenji Abe, Takeshi Shinohara, Shin-ichi Minato and T. Takagi. Their work appears in journals such as Theoretical Computer Science, Discrete Applied Mathematics, PLoS ONE, Algorithmica and Magnetic Resonance in Medical Sciences.

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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