Scalable and Efficient Approach for High Temporal Fuzzy Utility Pattern Mining-Reference-Cited by-同舟云学术

Scalable and Efficient Approach for High Temporal Fuzzy Utility Pattern Mining

Published:2022 Issue: Volume: Page:1-14
ISSN:2168-2267
Container-title:IEEE Transactions on Cybernetics
language:
Short-container-title:IEEE Trans. Cybern.

Author:

Ryu Taewoong¹,Kim Heonho¹,Lee Chanhee¹,Kim Heonmo¹,Vo Bay²^ORCID,Lin Jerry Chun-Wei³^ORCID,Pedrycz Witold⁴^ORCID,Yun Unil¹^ORCID

Affiliation:

1. Department of Computer Engineering, Sejong University, Seoul, South Korea

2. Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Vietnam

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

4. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

Funder

Ministry of Education, Science and Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Human-Computer Interaction,Information Systems,Control and Systems Engineering,Software

Link

http://xplorestaging.ieee.org/ielx7/6221036/6352949/09870754.pdf?arnumber=9870754

Cited by 8 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. Mining Interesting Sequential Patterns using a Novel Balanced Utility Measure;Knowledge-Based Systems;2024-06

2. An efficient method for mining High-Utility itemsets from unstable negative profit databases;Expert Systems with Applications;2024-03

3. Efficient approach of high average utility pattern mining with indexed list-based structure in dynamic environments;Information Sciences;2024-02

4. Improved adaptive-phase fuzzy high utility pattern mining algorithm based on tree-list structure for intelligent decision systems;Scientific Reports;2024-01-10

5. Frequent Closed High-Utility Itemset Mining Algorithm Based on Leiden Community Detection and Compact Genetic Algorithm;IEEE Access;2024