clustering validity function is an index used to judge the accuracy of clustering results. At present, most studies on clusteringvalidity are based on single clustering validity function. Research shows that no clust...
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clustering validity function is an index used to judge the accuracy of clustering results. At present, most studies on clusteringvalidity are based on single clustering validity function. Research shows that no clustering validity function can handle any data and always perform better than other indexes. Therefore, a hybrid weighted combination evaluation method based on fuzzy C-means (FCM) clustering validity functions was proposed. The weighting method combines expert weighting with information entropy weighting to improve the subjective factor influence of expert weighting and the shortcoming of information entropy weighting in the value judgment of each clustering validity function. Four clustering validity function combination methods of linear, exponential, logarithm and proportion was studied. Finally, the proposed fuzzy clusteringvalidity evaluation method is verified by experiments on artificial data sets and UCI data sets. The experimental results show that the proposed fuzzy clusteringvalidity evaluation method can overcome the shortcoming of single clustering validity function, and can get the optimal clustering number more accurately for different data sets.
This study contributed to the comprehensive assessment of flood risk in the Huaihongnanpian flood control protected area (simplified as the HHNP) of the Huaihe River Basin in China. Flood risk analyses were performed ...
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This study contributed to the comprehensive assessment of flood risk in the Huaihongnanpian flood control protected area (simplified as the HHNP) of the Huaihe River Basin in China. Flood risk analyses were performed by incorporating flood hazard and vulnerability. Flood hazard was simulated by a 1D-2D coupled hydrodynamic model. Flow velocity, inundation duration, and inundation depth were taken as hazard indicators, while agricultural population proportions, female population proportions, GDP per unit area, GDP per person, population density, residential density, shelter density, the land-use sensitivity index, road network density, and river network density were used as vulnerability indicators. Based on these indicators, a regional flood risk assessment model was put forward, which coupled fuzzy c-means clustering, factor analysis, and a clustering validity function. As an example, a proposed model was applied to evaluate the degree of flood risk for 15 townships in the HHNP. The research results showed that (1) flood risk in the HHNP was closely related to three main factors: socioeconomic factor, land cover factor, and flood factor;(2) the degree of risk was objectively divided into six zones: especially high, high, relatively high, medium, relatively low and low;and (3) in the 15 townships, Xiaobengbu (XB), Chengguan (CG), and Wuxiaojie (WX) fell into the especially high, high, and relatively high zones, respectively. Xinji (XJ), Toupu (TP), Daxin (DX), Caoguzhang (CGZ), Meiqiao (MQ), Caolaoji (CL), and Mohekou (MH) fell into the medium-risk zone. Linbeihuizu (LB) was categorized into the relatively low-risk zone, and Xinmaqiao (XM), Wangzhuang (WZ), Kuainan (KN), and Weizhuang (WZ) fell into the low-risk zone. The research results revealed the main driving factors and the spatial distribution of flood risk in the HHNP;therefore, it is highly significant for us to understand the main flood risk sources to provide guidance for flood control and management in t
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