Parallel Frequent Pattern Mining is a computational framework within frequent pattern mining that decomposes the task of discovering frequent itemsets into concurrent subtasks processed across multiple processors or nodes to improve scalability and efficiency.
176
Publications
8.9K
Citations
434
Authors
200
Institutions
Publications per year
2017–2026
44
Parents
Publications
176
Parallel and distributed association mining: a survey
Mohammed J. Zaki · IEEE Concurrency · 1999 · 478 citations
Haoyuan Li, Yi Wang, Dong Zhang et al. · 2008 · 441 citations
Scalable parallel data mining for association rules
Eui-Hong Han, George Karypis, Vipin Kumar · 1997 · 264 citations
1–5 of 176
Authors
434
- SP
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- MJ
6
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- XQ
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- SO
4
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- RG
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1–5 of 434
Institutions
200
Tsinghua University
Beijing, China
6 h-index
14 publications
471 citations
Auburn University
Auburn, United States
5 h-index
5 publications
233 citations
Taiyuan University of Science and Technology
Taiyuan, China
4 h-index
11 publications
511 citations
Ton Duc Thang University
Ho Chi Minh City, Vietnam
4 h-index
7 publications
185 citations
University of Alberta
Edmonton, Canada
4 h-index
11 publications
598 citations
Ca' Foscari University of Venice
Venice, Italy
4 h-index
7 publications
734 citations
Laboratoire d'Informatique de Grenoble
Grenoble, France
4 h-index
13 publications
524 citations
The University of Tokyo
Tokyo, Japan
4 h-index
8 publications
430 citations
1–8 of 200
Venues
- Information Sciences
7 publications · 235 citations
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- Knowledge and Information Systems
5 publications · 229 citations
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- The Journal of Supercomputing
5 publications · 140 citations
5
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- Expert Systems with Applications
4 publications · 99 citations
4
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- IEEE Transactions on Knowledge and Data Engineering
TKDE · 4 publications · 1.5K citations
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