An Adjusted Apriori Algorithm to Itemsets Defined by Tables and an Improved Rule Generator with Three-Way Decisions

The NIS-Apriori algorithm, which is extended from the Apriori algorithm, was proposed for rule generation from non-deterministic information systems and implemented in SQL. The realized system handles the concept of certainty, possibility, and three-way decisions. This paper newly focuses on such a...

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Autores principales: Jian, Zhiwen, Sakai, Hiroshi, Ohwa, Takuya, Shen, Kao-Yi, Nakata, Michinori
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338158/
http://dx.doi.org/10.1007/978-3-030-52705-1_7
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author Jian, Zhiwen
Sakai, Hiroshi
Ohwa, Takuya
Shen, Kao-Yi
Nakata, Michinori
author_facet Jian, Zhiwen
Sakai, Hiroshi
Ohwa, Takuya
Shen, Kao-Yi
Nakata, Michinori
author_sort Jian, Zhiwen
collection PubMed
description The NIS-Apriori algorithm, which is extended from the Apriori algorithm, was proposed for rule generation from non-deterministic information systems and implemented in SQL. The realized system handles the concept of certainty, possibility, and three-way decisions. This paper newly focuses on such a characteristic of table data sets that there is usually a fixed decision attribute. Therefore, it is enough for us to handle itemsets with one decision attribute, and we can see that one frequent itemset defines one implication. We make use of these characteristics and reduce the unnecessary itemsets for improving the performance of execution. Some experiments by the implemented software tool in Python clarify the improved performance.
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spelling pubmed-73381582020-07-07 An Adjusted Apriori Algorithm to Itemsets Defined by Tables and an Improved Rule Generator with Three-Way Decisions Jian, Zhiwen Sakai, Hiroshi Ohwa, Takuya Shen, Kao-Yi Nakata, Michinori Rough Sets Article The NIS-Apriori algorithm, which is extended from the Apriori algorithm, was proposed for rule generation from non-deterministic information systems and implemented in SQL. The realized system handles the concept of certainty, possibility, and three-way decisions. This paper newly focuses on such a characteristic of table data sets that there is usually a fixed decision attribute. Therefore, it is enough for us to handle itemsets with one decision attribute, and we can see that one frequent itemset defines one implication. We make use of these characteristics and reduce the unnecessary itemsets for improving the performance of execution. Some experiments by the implemented software tool in Python clarify the improved performance. 2020-06-10 /pmc/articles/PMC7338158/ http://dx.doi.org/10.1007/978-3-030-52705-1_7 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Jian, Zhiwen
Sakai, Hiroshi
Ohwa, Takuya
Shen, Kao-Yi
Nakata, Michinori
An Adjusted Apriori Algorithm to Itemsets Defined by Tables and an Improved Rule Generator with Three-Way Decisions
title An Adjusted Apriori Algorithm to Itemsets Defined by Tables and an Improved Rule Generator with Three-Way Decisions
title_full An Adjusted Apriori Algorithm to Itemsets Defined by Tables and an Improved Rule Generator with Three-Way Decisions
title_fullStr An Adjusted Apriori Algorithm to Itemsets Defined by Tables and an Improved Rule Generator with Three-Way Decisions
title_full_unstemmed An Adjusted Apriori Algorithm to Itemsets Defined by Tables and an Improved Rule Generator with Three-Way Decisions
title_short An Adjusted Apriori Algorithm to Itemsets Defined by Tables and an Improved Rule Generator with Three-Way Decisions
title_sort adjusted apriori algorithm to itemsets defined by tables and an improved rule generator with three-way decisions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338158/
http://dx.doi.org/10.1007/978-3-030-52705-1_7
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