The water supply association analysis method in Shenzhen based on kmeans clustering discretization and apriori algorithm.

Since water supply association analysis plays an important role in attribution analysis of water supply fluctuation, how to carry out effective association analysis has become a critical problem. However, the current techniques and methods used for association analysis are not very effective because...

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Autores principales:	Xin Liu, Xuefeng Sang, Jiaxuan Chang, Yang Zheng, Yuping Han
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9d812cd399de4814bfb0ddf2d3768c56
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spelling	oai:doaj.org-article:9d812cd399de4814bfb0ddf2d3768c562021-12-02T20:15:12ZThe water supply association analysis method in Shenzhen based on kmeans clustering discretization and apriori algorithm.1932-620310.1371/journal.pone.0255684https://doaj.org/article/9d812cd399de4814bfb0ddf2d3768c562021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0255684https://doaj.org/toc/1932-6203Since water supply association analysis plays an important role in attribution analysis of water supply fluctuation, how to carry out effective association analysis has become a critical problem. However, the current techniques and methods used for association analysis are not very effective because they are based on continuous data. In general, there is different degrees of monotone relationship between continuous data, which makes the analysis results easily affected by monotone relationship. The multicollinearity between continuous data distorts these analytical methods and may generate incorrect results. Meanwhile, we cannot know the association rules and value interval between features and water supply. Therefore, the lack of an effective analysis method hinders the water supply association analysis. Association rules and value interval of features obtained from association analysis are helpful to grasp cause of water supply fluctuation and know the fluctuation interval of water supply, so as to provide better support for water supply dispatching. But the association rules and value interval between features and water supply are not fully understood. In this study, a data mining method coupling kmeans clustering discretization and apriori algorithm was proposed. The kmeans was used for data discretization to obtain the one-hot encoding that can be recognized by apriori, and the discretization can also avoid the influence of monotone relationship and multicollinearity on analysis results. All the rules eventually need to be validated in order to filter out spurious rules. The results show that the method in this study is an effective association analysis method. The method can not only obtain the valid strong association rules between features and water supply, but also understand whether the association relationship between features and water supply is direct or indirect. Meanwhile, the method can also obtain value interval of features, the association degree between features and confidence probability of rules.Xin LiuXuefeng SangJiaxuan ChangYang ZhengYuping HanPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0255684 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xin Liu Xuefeng Sang Jiaxuan Chang Yang Zheng Yuping Han The water supply association analysis method in Shenzhen based on kmeans clustering discretization and apriori algorithm.
description	Since water supply association analysis plays an important role in attribution analysis of water supply fluctuation, how to carry out effective association analysis has become a critical problem. However, the current techniques and methods used for association analysis are not very effective because they are based on continuous data. In general, there is different degrees of monotone relationship between continuous data, which makes the analysis results easily affected by monotone relationship. The multicollinearity between continuous data distorts these analytical methods and may generate incorrect results. Meanwhile, we cannot know the association rules and value interval between features and water supply. Therefore, the lack of an effective analysis method hinders the water supply association analysis. Association rules and value interval of features obtained from association analysis are helpful to grasp cause of water supply fluctuation and know the fluctuation interval of water supply, so as to provide better support for water supply dispatching. But the association rules and value interval between features and water supply are not fully understood. In this study, a data mining method coupling kmeans clustering discretization and apriori algorithm was proposed. The kmeans was used for data discretization to obtain the one-hot encoding that can be recognized by apriori, and the discretization can also avoid the influence of monotone relationship and multicollinearity on analysis results. All the rules eventually need to be validated in order to filter out spurious rules. The results show that the method in this study is an effective association analysis method. The method can not only obtain the valid strong association rules between features and water supply, but also understand whether the association relationship between features and water supply is direct or indirect. Meanwhile, the method can also obtain value interval of features, the association degree between features and confidence probability of rules.
format	article
author	Xin Liu Xuefeng Sang Jiaxuan Chang Yang Zheng Yuping Han
author_facet	Xin Liu Xuefeng Sang Jiaxuan Chang Yang Zheng Yuping Han
author_sort	Xin Liu
title	The water supply association analysis method in Shenzhen based on kmeans clustering discretization and apriori algorithm.
title_short	The water supply association analysis method in Shenzhen based on kmeans clustering discretization and apriori algorithm.
title_full	The water supply association analysis method in Shenzhen based on kmeans clustering discretization and apriori algorithm.
title_fullStr	The water supply association analysis method in Shenzhen based on kmeans clustering discretization and apriori algorithm.
title_full_unstemmed	The water supply association analysis method in Shenzhen based on kmeans clustering discretization and apriori algorithm.
title_sort	water supply association analysis method in shenzhen based on kmeans clustering discretization and apriori algorithm.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/9d812cd399de4814bfb0ddf2d3768c56
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The water supply association analysis method in Shenzhen based on kmeans clustering discretization and apriori algorithm.

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