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Rule-ranking method based on item utility in adaptive rule model

BACKGROUND: Decision-making is an important part of most human activities regardless of their daily activities, profession, or political inclination. Some decisions are relatively simple specifically when the consequences are insignificant while others can be very complex and have significant effect...

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Autores principales: Hikmawati, Erna, Maulidevi, Nur Ulfa, Surendro, Kridanto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299285/
https://www.ncbi.nlm.nih.gov/pubmed/35875632
http://dx.doi.org/10.7717/peerj-cs.1013
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author Hikmawati, Erna
Maulidevi, Nur Ulfa
Surendro, Kridanto
author_facet Hikmawati, Erna
Maulidevi, Nur Ulfa
Surendro, Kridanto
author_sort Hikmawati, Erna
collection PubMed
description BACKGROUND: Decision-making is an important part of most human activities regardless of their daily activities, profession, or political inclination. Some decisions are relatively simple specifically when the consequences are insignificant while others can be very complex and have significant effects. Real-life decision problems generally involve several conflicting points of view (criteria) needed to be considered and this is the reason recent decision-making processes are usually supported by data as indicated by different data mining techniques. Data mining is the process of extracting data to obtain useful information and a promising and widely applied method is association rule mining which has the ability to identify interesting relationships between sets of items in a dataset and predict the associative behavior for new data. However, the number of rules generated in association rules can be very large, thereby making the exploitation process difficult. This means it is necessary to prioritize the selection of more valuable and relevant rules. METHODS: Therefore, this study proposes a method to rank rules based on the lift ratio value calculated from the frequency and utility of the item. The three main functions in proposed method are mining of association rules from different databases (in terms of sources, characteristics, and attributes), automatic threshold value determination process, and prioritization of the rules produced. RESULTS: Experiments conducted on six datasets showed that the number of rules generated by the adaptive rule model is higher and sorted from the largest lift ratio value compared to the apriori algorithm.
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spelling pubmed-92992852022-07-21 Rule-ranking method based on item utility in adaptive rule model Hikmawati, Erna Maulidevi, Nur Ulfa Surendro, Kridanto PeerJ Comput Sci Algorithms and Analysis of Algorithms BACKGROUND: Decision-making is an important part of most human activities regardless of their daily activities, profession, or political inclination. Some decisions are relatively simple specifically when the consequences are insignificant while others can be very complex and have significant effects. Real-life decision problems generally involve several conflicting points of view (criteria) needed to be considered and this is the reason recent decision-making processes are usually supported by data as indicated by different data mining techniques. Data mining is the process of extracting data to obtain useful information and a promising and widely applied method is association rule mining which has the ability to identify interesting relationships between sets of items in a dataset and predict the associative behavior for new data. However, the number of rules generated in association rules can be very large, thereby making the exploitation process difficult. This means it is necessary to prioritize the selection of more valuable and relevant rules. METHODS: Therefore, this study proposes a method to rank rules based on the lift ratio value calculated from the frequency and utility of the item. The three main functions in proposed method are mining of association rules from different databases (in terms of sources, characteristics, and attributes), automatic threshold value determination process, and prioritization of the rules produced. RESULTS: Experiments conducted on six datasets showed that the number of rules generated by the adaptive rule model is higher and sorted from the largest lift ratio value compared to the apriori algorithm. PeerJ Inc. 2022-06-28 /pmc/articles/PMC9299285/ /pubmed/35875632 http://dx.doi.org/10.7717/peerj-cs.1013 Text en © 2022 Hikmawati 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
Hikmawati, Erna
Maulidevi, Nur Ulfa
Surendro, Kridanto
Rule-ranking method based on item utility in adaptive rule model
title Rule-ranking method based on item utility in adaptive rule model
title_full Rule-ranking method based on item utility in adaptive rule model
title_fullStr Rule-ranking method based on item utility in adaptive rule model
title_full_unstemmed Rule-ranking method based on item utility in adaptive rule model
title_short Rule-ranking method based on item utility in adaptive rule model
title_sort rule-ranking method based on item utility in adaptive rule model
topic Algorithms and Analysis of Algorithms
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299285/
https://www.ncbi.nlm.nih.gov/pubmed/35875632
http://dx.doi.org/10.7717/peerj-cs.1013
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