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Efficiently mining erasable stream patterns for intelligent systems over uncertain data

  • Published:2020-07-22 Issue:11 Volume:35 Page:1699-1734
  • ISSN:0884-8173
  • Container-title:International Journal of Intelligent Systems
  • language:en
  • Short-container-title:Int J Intell Syst

Author:

Baek Yoonji1,Yun Unil1,Lin Jerry Chun‐Wei2,Yoon Eunchul3,Fujita Hamido4

Affiliation:

1. Department of Computer Engineering Sejong University Seoul Republic of Korea

2. Department of Computer Science, Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

3. Department of Electronics Engineering Konkuk University Seoul Republic of Korea

4. Faculty of Software and Information Science Iwate Prefectural University Iwate Japan

Funder

National Research Foundation of Korea

Publisher

Wiley

Subject

Artificial Intelligence,Human-Computer Interaction,Theoretical Computer Science,Software

Cited by 15 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An efficient approach for incremental erasable utility pattern mining from non-binary data;Knowledge and Information Systems;2024-08-04

2. A Parallel Mining Algorithm for Maximum Erasable Itemset Based on Multi-core Processor;Tehnicki vjesnik - Technical Gazette;2024-04-15

3. Discovering Interesting Patterns from Hypergraphs;ACM Transactions on Knowledge Discovery from Data;2023-10-16

4. Mining frequent weighted utility patterns with dynamic weighted items from quantitative databases;Applied Intelligence;2023-03-10

5. Mining Frequent Weighted Utility Patterns in Dynamic Quantitative Databases;2022 RIVF International Conference on Computing and Communication Technologies (RIVF);2022-12-20
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