Zou Hui, Zou Zhihong, Wang Xiaojing
School of Economics and Management, Beihang University, Beijing 100191, China.
School of Science, China Agricultural University, Beijing 100083, China.
Int J Environ Res Public Health. 2015 Nov 12;12(11):14400-13. doi: 10.3390/ijerph121114400.
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.
由不确定环境导致的数据量增加和复杂性是当今的现实情况。为了有效且可靠地识别水质，本文提出了一种用于水质分析的改进快速聚类算法。该算法采用了变权重K均值聚类算法来分析水质监测数据。变权重方案是基于K均值的改进指标权重自调整算法（称为MIWAS-K均值）选择的最佳加权指标。新的聚类算法避免了在某些情况下迭代余量未被计算的问题。通过快速聚类分析，我们可以识别水样的质量。该算法应用于中国海河流域监测网络在八年（2006 - 2013年）期间获取的、在七个不同站点有四个指标的（2078个样本）水质分析。理论和模拟结果均表明，该算法对海河流域水质分析是高效且可靠的。此外，该算法可应用于更复杂的高维数据矩阵。
