Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases-Reference-Cited by-同舟云学术

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Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases

Published:2023 Issue: Volume:11 Page:12504-12524
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Dao Hong N.¹^ORCID,Ravikumar Penugonda²^ORCID,Likhitha Palla³^ORCID,Rage Uday Kiran⁴^ORCID,Watanobe Yutaka¹^ORCID,Paik Incheon¹^ORCID

Affiliation:

1. Department of Computer Science and Engineering, The University of Aizu, Fukushima, Aizuwakamatsu, Japan

2. IIIT-Idupulapaya, RGUKT, Andhra Pradesh, Nuzividu, India

3. National Institute of Information and Communications Technology, Tokyo, Japan

4. Institute of Industrial Science, The University of Tokyo, Tokyo, Japan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10034746.pdf?arnumber=10034746

Reference55 articles.

1. An efficient algorithm for mining association rules in large databases;savasere,1995

2. HARPP: HARnessing the Power of Power sets for Mining Frequent Itemsets

3. H-Mine: Fast and space-preserving frequent pattern mining in large databases

4. Mining frequent patterns without candidate generation

Cited by 5 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. 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. 3P-ECLAT: mining partial periodic patterns in columnar temporal databases;Applied Intelligence;2023-12-16

3. A fundamental approach to discover closed periodic-frequent patterns in very large temporal databases;Applied Intelligence;2023-09-07

4. k-PFPMiner: Top-k Periodic Frequent Patterns in Big Temporal Databases;IEEE Access;2023

5. Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set Complements;IEEE Access;2023

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