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LCTree-Based Approach for Mining Frequent Items in Real-Time
With the increase of real-time stream data, knowledge discovery from stream data becomes more and more important, which requires an efficient data structure to store transactions and scan sliding windows once to discover frequent itemsets. We present a new method named Linking Compact Tree (LCTree)....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9586759/ https://www.ncbi.nlm.nih.gov/pubmed/36275960 http://dx.doi.org/10.1155/2022/7430106 |
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author | Chen, Jiashun Chen, Jianjing Zhong, Zhaoman Zhang, Hao Kantardzic, Mehmed |
author_facet | Chen, Jiashun Chen, Jianjing Zhong, Zhaoman Zhang, Hao Kantardzic, Mehmed |
author_sort | Chen, Jiashun |
collection | PubMed |
description | With the increase of real-time stream data, knowledge discovery from stream data becomes more and more important, which requires an efficient data structure to store transactions and scan sliding windows once to discover frequent itemsets. We present a new method named Linking Compact Tree (LCTree). We designed an algorithm by using an improved data structure to create objective tree, which can find frequent itemsets with linear complexity. Secondly, we can merge items in sliding windows by one scan with Head Linking List data structure. Third, by implementing data structure of Tail Linking List, we can locate the obsolete nodes and remove them easily. Finally, LCTree is able to find all exact frequent items in data stream with reduced time and space complexity by using such a linear data structure. Experiments on datasets with different sizes and types were conducted to compare the proposed LCTree technique with well-known frequent item mining methods including Cantree, FP-tree, DSTree, CPSTree, and Gtree. The results of experiments show presented algorithm has better performance than other methods, and also confirm that it is a promising solution for detecting frequent item sets in real time applications. |
format | Online Article Text |
id | pubmed-9586759 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-95867592022-10-22 LCTree-Based Approach for Mining Frequent Items in Real-Time Chen, Jiashun Chen, Jianjing Zhong, Zhaoman Zhang, Hao Kantardzic, Mehmed Comput Intell Neurosci Research Article With the increase of real-time stream data, knowledge discovery from stream data becomes more and more important, which requires an efficient data structure to store transactions and scan sliding windows once to discover frequent itemsets. We present a new method named Linking Compact Tree (LCTree). We designed an algorithm by using an improved data structure to create objective tree, which can find frequent itemsets with linear complexity. Secondly, we can merge items in sliding windows by one scan with Head Linking List data structure. Third, by implementing data structure of Tail Linking List, we can locate the obsolete nodes and remove them easily. Finally, LCTree is able to find all exact frequent items in data stream with reduced time and space complexity by using such a linear data structure. Experiments on datasets with different sizes and types were conducted to compare the proposed LCTree technique with well-known frequent item mining methods including Cantree, FP-tree, DSTree, CPSTree, and Gtree. The results of experiments show presented algorithm has better performance than other methods, and also confirm that it is a promising solution for detecting frequent item sets in real time applications. Hindawi 2022-10-14 /pmc/articles/PMC9586759/ /pubmed/36275960 http://dx.doi.org/10.1155/2022/7430106 Text en Copyright © 2022 Jiashun Chen et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Chen, Jiashun Chen, Jianjing Zhong, Zhaoman Zhang, Hao Kantardzic, Mehmed LCTree-Based Approach for Mining Frequent Items in Real-Time |
title | LCTree-Based Approach for Mining Frequent Items in Real-Time |
title_full | LCTree-Based Approach for Mining Frequent Items in Real-Time |
title_fullStr | LCTree-Based Approach for Mining Frequent Items in Real-Time |
title_full_unstemmed | LCTree-Based Approach for Mining Frequent Items in Real-Time |
title_short | LCTree-Based Approach for Mining Frequent Items in Real-Time |
title_sort | lctree-based approach for mining frequent items in real-time |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9586759/ https://www.ncbi.nlm.nih.gov/pubmed/36275960 http://dx.doi.org/10.1155/2022/7430106 |
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