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An Enhanced K-Means Algorithm for Water Quality Analysis of The Haihe River in China
The increase and the complexity of data caused by the uncertain environment is today’s reality. In order to identify water quality effectively and reliably, this paper presents a modified fast clustering algorithm for water quality analysis. The algorithm has adopted a varying weights K-means cluste...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4661655/ https://www.ncbi.nlm.nih.gov/pubmed/26569283 http://dx.doi.org/10.3390/ijerph121114400 |
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author | Zou, Hui Zou, Zhihong Wang, Xiaojing |
author_facet | Zou, Hui Zou, Zhihong Wang, Xiaojing |
author_sort | Zou, Hui |
collection | PubMed |
description | The increase and the complexity of data caused by the uncertain environment is today’s reality. In order to identify water quality effectively and reliably, this paper presents a modified fast clustering algorithm for water quality analysis. The algorithm has adopted a varying weights K-means cluster algorithm to analyze water monitoring data. The varying weights scheme was the best weighting indicator selected by a modified indicator weight self-adjustment algorithm based on K-means, which is named MIWAS-K-means. The new clustering algorithm avoids the margin of the iteration not being calculated in some cases. With the fast clustering analysis, we can identify the quality of water samples. The algorithm is applied in water quality analysis of the Haihe River (China) data obtained by the monitoring network over a period of eight years (2006–2013) with four indicators at seven different sites (2078 samples). Both the theoretical and simulated results demonstrate that the algorithm is efficient and reliable for water quality analysis of the Haihe River. In addition, the algorithm can be applied to more complex data matrices with high dimensionality. |
format | Online Article Text |
id | pubmed-4661655 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-46616552015-12-10 An Enhanced K-Means Algorithm for Water Quality Analysis of The Haihe River in China Zou, Hui Zou, Zhihong Wang, Xiaojing Int J Environ Res Public Health Article The increase and the complexity of data caused by the uncertain environment is today’s reality. In order to identify water quality effectively and reliably, this paper presents a modified fast clustering algorithm for water quality analysis. The algorithm has adopted a varying weights K-means cluster algorithm to analyze water monitoring data. The varying weights scheme was the best weighting indicator selected by a modified indicator weight self-adjustment algorithm based on K-means, which is named MIWAS-K-means. The new clustering algorithm avoids the margin of the iteration not being calculated in some cases. With the fast clustering analysis, we can identify the quality of water samples. The algorithm is applied in water quality analysis of the Haihe River (China) data obtained by the monitoring network over a period of eight years (2006–2013) with four indicators at seven different sites (2078 samples). Both the theoretical and simulated results demonstrate that the algorithm is efficient and reliable for water quality analysis of the Haihe River. In addition, the algorithm can be applied to more complex data matrices with high dimensionality. MDPI 2015-11-12 2015-11 /pmc/articles/PMC4661655/ /pubmed/26569283 http://dx.doi.org/10.3390/ijerph121114400 Text en © 2015 by the authors; licensee MDPI, Basel, Switzerland. https://creativecommons.org/licenses/by/4.0/This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ). |
spellingShingle | Article Zou, Hui Zou, Zhihong Wang, Xiaojing An Enhanced K-Means Algorithm for Water Quality Analysis of The Haihe River in China |
title | An Enhanced K-Means Algorithm for Water Quality Analysis of The Haihe River in China |
title_full | An Enhanced K-Means Algorithm for Water Quality Analysis of The Haihe River in China |
title_fullStr | An Enhanced K-Means Algorithm for Water Quality Analysis of The Haihe River in China |
title_full_unstemmed | An Enhanced K-Means Algorithm for Water Quality Analysis of The Haihe River in China |
title_short | An Enhanced K-Means Algorithm for Water Quality Analysis of The Haihe River in China |
title_sort | enhanced k-means algorithm for water quality analysis of the haihe river in china |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4661655/ https://www.ncbi.nlm.nih.gov/pubmed/26569283 http://dx.doi.org/10.3390/ijerph121114400 |
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