华中科技大学学报（自然科学版）2009，Vol.37Issue(11)：35-38,4.
基于改进模糊C均值聚类算法的洪水过程分类
Classification of flood hydrograph using improved fuzzy C-means clustering algorithm
摘要
Abstract
A fuzzy clustering model for flood hydrograph classification was established based on fuzzy C-means (FCM) clustering algorithm. All observed samples can be classified into some clusters by using this model and the cluster prototype of each cluster can be regarded as one of typical hydro-graphs. Because the conventional FCM algorithm is prone to fall into local extreme points, especially when dealing with massive and high-dimensional data sets as in cluster analysis for flood hydrograph classification, genetic algorithms were used to improve it. An example was included to illustrate the improved FCM algorithm and the feasible clustering results were respectively evaluated by using a cluster validity function based on possibility distribution theory. The agreement of the optimal cluste-ring result and the actual data indicates that the proposed method performs well and can be used for flood hydrograph classification.关键词
洪水过程/聚类分析/遗传算法/模糊聚类/聚类有效性Key words
flood hydrograph/ cluster analysis/genetic algorithms/ fuzzy clustering/ cluster validity分类
建筑与水利引用本文复制引用
程卫帅,纪昌明,刘丹..基于改进模糊C均值聚类算法的洪水过程分类[J].华中科技大学学报（自然科学版）,2009,37(11):35-38,4.基金项目
国家自然科学基金资助项目(50579019). （50579019）
