Mining Maximal High Utility Itemsets on Dynamic Profit Databases-Reference-Cited by-同舟云学术

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Mining Maximal High Utility Itemsets on Dynamic Profit Databases

Published:2020-01-10 Issue:2 Volume:51 Page:140-160
ISSN:0196-9722
Container-title:Cybernetics and Systems
language:en
Short-container-title:Cybernetics and Systems

Author:

Nguyen Loan T. T.¹,Vu Dinh-Bao²,Nguyen Trinh D. D.³,Vo Bay⁴

Affiliation:

1. School of Computer Science and Engineering, International University, VNU-HCM, Ho Chi Minh City, Vietnam;

2. Faculty of Information Technology, CaoThang Technical College, Ho Chi Minh City, Vietnam;

3. Institute of Research and Development, Duy Tan University, Da Nang, Vietnam;

4. Faculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh City, Vietnam

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Information Systems,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/01969722.2019.1705549

Reference24 articles.

1. Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases

2. Infrequent Weighted Itemset Mining Using Frequent Pattern Growth

3. CLS-Miner: efficient and effective closed high-utility itemset mining

4. Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach

Cited by 14 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. MLC-miner: Efficiently discovering multi-level closed high utility patterns from quantitative hierarchical transaction databases;Expert Systems with Applications;2024-11

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

3. Efficiently Discover Multi-level Maximal High-Utility Patterns from Hierarchical Databases;Lecture Notes in Computer Science;2024

4. New approaches for mining high utility itemsets with multiple utility thresholds;Applied Intelligence;2023-12-19

5. Parallel approaches to extract multi-level high utility itemsets from hierarchical transaction databases;Knowledge-Based Systems;2023-09

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