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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Veröffentlicht in:PloS one Jg. 16; H. 8; S. e0255684
Hauptverfasser: Liu, Xin, Sang, Xuefeng, Chang, Jiaxuan, Zheng, Yang, Han, Yuping
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 05.08.2021
Public Library of Science (PLoS)
Schlagworte:
Algorithms
Biology and Life Sciences
China
Cluster Analysis
Computer and Information Sciences
Data Interpretation, Statistical
Data mining
Earth Sciences
Ecology and Environmental Sciences
Methods
Physical Sciences
Research and Analysis Methods
Statistics
Water Supply - statistics & numerical data
Water-supply
China
ISSN:1932-6203, 1932-6203
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Zusammenfassung: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.
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Competing Interests: No authors have competing interests
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0255684