Mining closed high utility patterns with negative utility in dynamic databases-Reference-Cited by-同舟云学术

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Mining closed high utility patterns with negative utility in dynamic databases

Published:2022-09-10 Issue: Volume: Page:
ISSN:0924-669X
Container-title:Applied Intelligence
language:en
Short-container-title:Appl Intell

Author:

Han Meng,Zhang Ni,Wang Le,Li Xiaojuan,Cheng Haodong

Funder

the National Natural Science Foundation of China

Postgraduate Innovation Project of North Minzu University

Computer Application Technology Autonomous Region Key Discipline Project

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Link

https://link.springer.com/content/pdf/10.1007/s10489-022-03876-8.pdf

Reference29 articles.

1. Ahmed CF, Tanbeer SK, Jeong BS, et al. (2009) Efficient tree structures for high utility pattern mining in incremental databases. IEEE Trans Knowl Data Eng 21(12):1708–1721

2. Chu CJ, Tseng VS, Liang T (2009) An efficient algorithm for mining high utility itemsets with negative item values in large databases. Appl Math Comput 215(2):767–778

3. Dam TL, Li K, Fournier-Viger P, et al. (2019) Cls-miner: efficient and effective closed high-utility itemset mining. Front Comput Sci 13(2):357–381

4. Dam TL, Ramampiaro H, Nørvåg K, et al. (2019) Towards efficiently mining closed high utility itemsets from incremental databases. Knowl-Based Syst 165:13–29

5. Djenouri Y, Comuzzi M (2017) Combining apriori heuristic and bio-inspired algorithms for solving the frequent itemsets mining problem. Inf Sci 420:1–15

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1. Stable top-k periodic high-utility patterns mining over multi-sequence;Intelligent Data Analysis;2024-07-23

2. Mining Top-K constrained cross-level high-utility itemsets over data streams;Knowledge and Information Systems;2024-01-21

3. Incremental Top-k High Utility Pattern Mining and Analyzing Over the Entire Accumulated Dynamic Database;IEEE Access;2024

4. Hug-Iml: Interpretable Machine Learning for Decision Support Using High Utility Gain Patterns;2024

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