Maximal Frequent Itemset Mining
Maximal Frequent Itemset Mining is a method in frequent pattern mining that identifies the smallest complete set of frequent itemsets by retaining only those itemsets for which no proper superset is frequent, thereby eliminating redundancy while preserving the support information of all frequent itemsets.
186
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13.4K
Citations
462
Authors
207
Institutions
Publications per year
2017–2026
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Frequent pattern mining: current status and future directions
Jiawei Han, Hong Cheng, Dong Xin et al. · Data Mining and Knowledge Discovery · 2007 · 1.4K citations · Full text
New algorithms for fast discovery of association rules
Mohammed J. Zaki, Srinivasan Parthasarathy, Mitsunori Ogihara et al. · 1997 · 1.1K citations
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Sejong University
Seoul, South Korea
10 h-index
20 publications
1.1K citations

Chungbuk National University
Cheongju-si, South Korea
6 h-index
9 publications
484 citations
Rensselaer Polytechnic Institute
Troy, United States
6 h-index
7 publications
970 citations
Wright State University
Dayton, United States
5 h-index
11 publications
294 citations

National Yang Ming Chiao Tung University
Hsinchu, Taiwan
5 h-index
11 publications
460 citations

University of Hong Kong
Pok Fu Lam, Hong Kong
4 h-index
14 publications
1.1K citations

Vietnam National University Ho Chi Minh City
Ho Chi Minh City, Vietnam
3 h-index
4 publications
70 citations
University at Buffalo, State University of New York
Buffalo, United States
3 h-index
3 publications
228 citations
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11 publications · 552 citations
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11 publications · 396 citations
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TKDE · 7 publications · 1.1K citations
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4 publications · 281 citations
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