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TKFIM: Top-K frequent itemset mining technique based on equivalence classes
Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially. For frequent Itemset (FIs) mining applications, it imposes new challenges. Misconceived information may be found in recent...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959650/ https://www.ncbi.nlm.nih.gov/pubmed/33817031 http://dx.doi.org/10.7717/peerj-cs.385 |
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author | Iqbal, Saood Shahid, Abdul Roman, Muhammad Khan, Zahid Al-Otaibi, Shaha Yu, Lisu |
author_facet | Iqbal, Saood Shahid, Abdul Roman, Muhammad Khan, Zahid Al-Otaibi, Shaha Yu, Lisu |
author_sort | Iqbal, Saood |
collection | PubMed |
description | Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially. For frequent Itemset (FIs) mining applications, it imposes new challenges. Misconceived information may be found in recent algorithms, including both threshold and size based algorithms. Threshold value plays a central role in generating frequent itemsets from the given dataset. Selecting a support threshold value is very complicated for those unaware of the dataset’s characteristics. The performance of algorithms for finding FIs without the support threshold is, however, deficient due to heavy computation. Therefore, we have proposed a method to discover FIs without the support threshold, called Top-k frequent itemsets mining (TKFIM). It uses class equivalence and set-theory concepts for mining FIs. The proposed procedure does not miss any FIs; thus, accurate frequent patterns are mined. Furthermore, the results are compared with state-of-the-art techniques such as Top-k miner and Build Once and Mine Once (BOMO). It is found that the proposed TKFIM has outperformed the results of these approaches in terms of execution and performance, achieving 92.70, 35.87, 28.53, and 81.27 percent gain on Top-k miner using Chess, Mushroom, and Connect and T1014D100K datasets, respectively. Similarly, it has achieved a performance gain of 97.14, 100, 78.10, 99.70 percent on BOMO using Chess, Mushroom, Connect, and T1014D100K datasets, respectively. Therefore, it is argued that the proposed procedure may be adopted on a large dataset for better performance. |
format | Online Article Text |
id | pubmed-7959650 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79596502021-04-02 TKFIM: Top-K frequent itemset mining technique based on equivalence classes Iqbal, Saood Shahid, Abdul Roman, Muhammad Khan, Zahid Al-Otaibi, Shaha Yu, Lisu PeerJ Comput Sci Algorithms and Analysis of Algorithms Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially. For frequent Itemset (FIs) mining applications, it imposes new challenges. Misconceived information may be found in recent algorithms, including both threshold and size based algorithms. Threshold value plays a central role in generating frequent itemsets from the given dataset. Selecting a support threshold value is very complicated for those unaware of the dataset’s characteristics. The performance of algorithms for finding FIs without the support threshold is, however, deficient due to heavy computation. Therefore, we have proposed a method to discover FIs without the support threshold, called Top-k frequent itemsets mining (TKFIM). It uses class equivalence and set-theory concepts for mining FIs. The proposed procedure does not miss any FIs; thus, accurate frequent patterns are mined. Furthermore, the results are compared with state-of-the-art techniques such as Top-k miner and Build Once and Mine Once (BOMO). It is found that the proposed TKFIM has outperformed the results of these approaches in terms of execution and performance, achieving 92.70, 35.87, 28.53, and 81.27 percent gain on Top-k miner using Chess, Mushroom, and Connect and T1014D100K datasets, respectively. Similarly, it has achieved a performance gain of 97.14, 100, 78.10, 99.70 percent on BOMO using Chess, Mushroom, Connect, and T1014D100K datasets, respectively. Therefore, it is argued that the proposed procedure may be adopted on a large dataset for better performance. PeerJ Inc. 2021-03-08 /pmc/articles/PMC7959650/ /pubmed/33817031 http://dx.doi.org/10.7717/peerj-cs.385 Text en ©2021 Iqbal et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Algorithms and Analysis of Algorithms Iqbal, Saood Shahid, Abdul Roman, Muhammad Khan, Zahid Al-Otaibi, Shaha Yu, Lisu TKFIM: Top-K frequent itemset mining technique based on equivalence classes |
title | TKFIM: Top-K frequent itemset mining technique based on equivalence classes |
title_full | TKFIM: Top-K frequent itemset mining technique based on equivalence classes |
title_fullStr | TKFIM: Top-K frequent itemset mining technique based on equivalence classes |
title_full_unstemmed | TKFIM: Top-K frequent itemset mining technique based on equivalence classes |
title_short | TKFIM: Top-K frequent itemset mining technique based on equivalence classes |
title_sort | tkfim: top-k frequent itemset mining technique based on equivalence classes |
topic | Algorithms and Analysis of Algorithms |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7959650/ https://www.ncbi.nlm.nih.gov/pubmed/33817031 http://dx.doi.org/10.7717/peerj-cs.385 |
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