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Open AccessArticle

An Enhanced K-Means Algorithm for Water Quality Analysis of The Haihe River in China

1
School of Economics and Management, Beihang University, Beijing 100191, China
2
School of Science, China Agricultural University, Beijing 100083, China
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Author to whom correspondence should be addressed.
Academic Editor: Miklas Scholz
Int. J. Environ. Res. Public Health 2015, 12(11), 14400-14413; https://doi.org/10.3390/ijerph121114400
Received: 23 June 2015 / Revised: 1 November 2015 / Accepted: 6 November 2015 / Published: 12 November 2015
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. View Full-Text
Keywords: water classification; indicator weight; local optimization water classification; indicator weight; local optimization
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Zou, H.; Zou, Z.; Wang, X. An Enhanced K-Means Algorithm for Water Quality Analysis of The Haihe River in China. Int. J. Environ. Res. Public Health 2015, 12, 14400-14413.

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