A novel association rule mining approach using TID intermediate itemset

Designing an efficient association rule mining (ARM) algorithm for multilevel knowledge-based transactional databases that is appropriate for real-world deployments is of paramount concern. However, dynamic decision making that needs to modify the threshold either to minimize or maximize the output...

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Main Authors: Aqra, Iyad, Herawan, Tutut, Norjihan, Abdul Ghani, Akhunzada, Adnan, Ali, Akhtar, Ramdan, Razali, Ilahi, Manzoor, Choo, Kim-Kwang Raymond
Format: Article
Language:English
Published: Public Library of Science 2018
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/25213/1/A%20novel%20association%20rule%20mining%20approach.pdf
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author Aqra, Iyad
Herawan, Tutut
Norjihan, Abdul Ghani
Akhunzada, Adnan
Ali, Akhtar
Ramdan, Razali
Ilahi, Manzoor
Choo, Kim-Kwang Raymond
author_facet Aqra, Iyad
Herawan, Tutut
Norjihan, Abdul Ghani
Akhunzada, Adnan
Ali, Akhtar
Ramdan, Razali
Ilahi, Manzoor
Choo, Kim-Kwang Raymond
author_sort Aqra, Iyad
collection UMP
description Designing an efficient association rule mining (ARM) algorithm for multilevel knowledge-based transactional databases that is appropriate for real-world deployments is of paramount concern. However, dynamic decision making that needs to modify the threshold either to minimize or maximize the output knowledge certainly necessitates the extant state-of-the-art algorithms to rescan the entire database. Subsequently, the process incurs heavy computation cost and is not feasible for real-time applications. The paper addresses efficiently the problem of threshold dynamic updation for a given purpose. The paper contributes by presenting a novel ARM approach that creates an intermediate itemset and applies a threshold to extract categorical frequent itemsets with diverse threshold values. Thus, improving the overall efficiency as we no longer needs to scan the whole database. After the entire itemset is built, we are able to obtain real support without the need of rebuilding the itemset (e.g. Itemset list is intersected to obtain the actual support). Moreover, the algorithm supports to extract many frequent itemsets according to a pre-determined minimum support with an independent purpose. Additionally, the experimental results of our proposed approach demonstrate the capability to be deployed in any mining system in a fully parallel mode; consequently, increasing the efficiency of the real-time association rules discovery process. The proposed approach outperforms the extant state-of-the-art and shows promising results that reduce computation cost, increase accuracy, and produce all possible itemsets.
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spelling UMPir252132019-07-09T06:18:44Z http://umpir.ump.edu.my/id/eprint/25213/ A novel association rule mining approach using TID intermediate itemset Aqra, Iyad Herawan, Tutut Norjihan, Abdul Ghani Akhunzada, Adnan Ali, Akhtar Ramdan, Razali Ilahi, Manzoor Choo, Kim-Kwang Raymond TK Electrical engineering. Electronics Nuclear engineering Designing an efficient association rule mining (ARM) algorithm for multilevel knowledge-based transactional databases that is appropriate for real-world deployments is of paramount concern. However, dynamic decision making that needs to modify the threshold either to minimize or maximize the output knowledge certainly necessitates the extant state-of-the-art algorithms to rescan the entire database. Subsequently, the process incurs heavy computation cost and is not feasible for real-time applications. The paper addresses efficiently the problem of threshold dynamic updation for a given purpose. The paper contributes by presenting a novel ARM approach that creates an intermediate itemset and applies a threshold to extract categorical frequent itemsets with diverse threshold values. Thus, improving the overall efficiency as we no longer needs to scan the whole database. After the entire itemset is built, we are able to obtain real support without the need of rebuilding the itemset (e.g. Itemset list is intersected to obtain the actual support). Moreover, the algorithm supports to extract many frequent itemsets according to a pre-determined minimum support with an independent purpose. Additionally, the experimental results of our proposed approach demonstrate the capability to be deployed in any mining system in a fully parallel mode; consequently, increasing the efficiency of the real-time association rules discovery process. The proposed approach outperforms the extant state-of-the-art and shows promising results that reduce computation cost, increase accuracy, and produce all possible itemsets. Public Library of Science 2018 Article PeerReviewed pdf en cc_public_domain http://umpir.ump.edu.my/id/eprint/25213/1/A%20novel%20association%20rule%20mining%20approach.pdf Aqra, Iyad and Herawan, Tutut and Norjihan, Abdul Ghani and Akhunzada, Adnan and Ali, Akhtar and Ramdan, Razali and Ilahi, Manzoor and Choo, Kim-Kwang Raymond (2018) A novel association rule mining approach using TID intermediate itemset. PLoS ONE, 13 (1). pp. 1-32. ISSN 1932-6203. (Published) https://doi.org/10.1371/journal.pone.0179703 https://doi.org/10.1371/journal.pone.0179703
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Aqra, Iyad
Herawan, Tutut
Norjihan, Abdul Ghani
Akhunzada, Adnan
Ali, Akhtar
Ramdan, Razali
Ilahi, Manzoor
Choo, Kim-Kwang Raymond
A novel association rule mining approach using TID intermediate itemset
title A novel association rule mining approach using TID intermediate itemset
title_full A novel association rule mining approach using TID intermediate itemset
title_fullStr A novel association rule mining approach using TID intermediate itemset
title_full_unstemmed A novel association rule mining approach using TID intermediate itemset
title_short A novel association rule mining approach using TID intermediate itemset
title_sort novel association rule mining approach using tid intermediate itemset
topic TK Electrical engineering. Electronics Nuclear engineering
url http://umpir.ump.edu.my/id/eprint/25213/1/A%20novel%20association%20rule%20mining%20approach.pdf
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