Approximate high utility itemset mining in noisy environments-Reference-Cited by-同舟云学术

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Approximate high utility itemset mining in noisy environments

Published:2021-01 Issue: Volume:212 Page:106596
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Baek Yoonji,Yun Unil,Kim Heonho,Kim Jongseong,Vo Bay,Truong Tin,Deng Zhi-Hong

Funder

National Research Foundation of Korea

Ministry of Education, Science and Technology, South Korea

Publisher

Elsevier BV

Subject

Artificial Intelligence,Information Systems and Management,Management Information Systems,Software

Reference52 articles.

1. Efficient algorithm for mining non-redundant high-utility association rules;Mai;Sensors,2020

2. Data mining based on clustering and association rule analysis for knowledge discovery in multiobjective topology optimization;Sato;Expert Syst. Appl.,2019

3. Association rule mining based parameter adaptive strategy for differential evolution algorithms;Wang;Expert Syst. Appl.,2019

4. Mining frequent patterns in process models;Chapela-Campa;Inform. Sci.,2019

5. Frequent itemset mining in big data with effective single scan algorithms;Djenouri;IEEE Access,2018

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1. Incremental mining algorithms for generating and updating frequent patterns for dynamic databases against insert, update, and support changes;International Journal of Data Science and Analytics;2024-08-13

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

3. Efficient mining of concise and informative representations of frequent high utility itemsets;Engineering Applications of Artificial Intelligence;2023-11

4. Fast privacy-preserving utility mining algorithm based on utility-list dictionary;Applied Intelligence;2023-10-27

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

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