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Enterprise Operating State Evaluation Based on Association Rule Algorithm and Data Set

In order to further improve the evaluation quality of enterprise operating efficiency, reduce the error items and invalid items of partition, and improve the objectivity of operating condition evaluation, this study takes listed enterprises as an example and proposes an evaluation method of operatin...

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Detalles Bibliográficos
Autores principales: Li, Shoubo, Zhu, Haiyan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9522508/
https://www.ncbi.nlm.nih.gov/pubmed/36188681
http://dx.doi.org/10.1155/2022/1300068
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author Li, Shoubo
Zhu, Haiyan
author_facet Li, Shoubo
Zhu, Haiyan
author_sort Li, Shoubo
collection PubMed
description In order to further improve the evaluation quality of enterprise operating efficiency, reduce the error items and invalid items of partition, and improve the objectivity of operating condition evaluation, this study takes listed enterprises as an example and proposes an evaluation method of operating efficiency based on association rule algorithm and data set. In this method, the results of operating efficiency are scientifically analyzed from horizontal and vertical dimensions. The operating cost of total assets of listed companies is taken as indicators, and the correlation test is carried out by Kendall's tau_b. From the longitudinal comparison results, it can be seen that only 12 of the 19 enterprises in the study have small-scale changes and increase year by year, accounting for 63.16%. At the same time, there are also 6 enterprises with an overall trend of decline, which objectively reflects the reasonable operation status and operation scale of enterprises in the study.
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spelling pubmed-95225082022-09-30 Enterprise Operating State Evaluation Based on Association Rule Algorithm and Data Set Li, Shoubo Zhu, Haiyan Comput Intell Neurosci Research Article In order to further improve the evaluation quality of enterprise operating efficiency, reduce the error items and invalid items of partition, and improve the objectivity of operating condition evaluation, this study takes listed enterprises as an example and proposes an evaluation method of operating efficiency based on association rule algorithm and data set. In this method, the results of operating efficiency are scientifically analyzed from horizontal and vertical dimensions. The operating cost of total assets of listed companies is taken as indicators, and the correlation test is carried out by Kendall's tau_b. From the longitudinal comparison results, it can be seen that only 12 of the 19 enterprises in the study have small-scale changes and increase year by year, accounting for 63.16%. At the same time, there are also 6 enterprises with an overall trend of decline, which objectively reflects the reasonable operation status and operation scale of enterprises in the study. Hindawi 2022-09-22 /pmc/articles/PMC9522508/ /pubmed/36188681 http://dx.doi.org/10.1155/2022/1300068 Text en Copyright © 2022 Shoubo Li and Haiyan Zhu. 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
Li, Shoubo
Zhu, Haiyan
Enterprise Operating State Evaluation Based on Association Rule Algorithm and Data Set
title Enterprise Operating State Evaluation Based on Association Rule Algorithm and Data Set
title_full Enterprise Operating State Evaluation Based on Association Rule Algorithm and Data Set
title_fullStr Enterprise Operating State Evaluation Based on Association Rule Algorithm and Data Set
title_full_unstemmed Enterprise Operating State Evaluation Based on Association Rule Algorithm and Data Set
title_short Enterprise Operating State Evaluation Based on Association Rule Algorithm and Data Set
title_sort enterprise operating state evaluation based on association rule algorithm and data set
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9522508/
https://www.ncbi.nlm.nih.gov/pubmed/36188681
http://dx.doi.org/10.1155/2022/1300068
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