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论文类型：期刊论文

发表时间：2012-01-01

发表刊物：STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT

收录刊物：Scopus、SCIE、EI

卷号：26

期号：1

页面范围：139-155

ISSN号：1436-3240

关键字：Gath-Geva (GG) clustering; Minimum message length (MML) criterion; Time series segmentation; Expectation maximization (EM) algorithm; Segmentation order

摘要：In this paper, an improved Gath-Geva clustering algorithm is proposed for automatic fuzzy segmentation of univariate and multivariate hydrometeorological time series. The algorithm considers time series segmentation problem as Gath-Geva clustering with the minimum message length criterion as segmentation order selection criterion. One characteristic of the improved Gath-Geva clustering algorithm is its unsupervised nature which can automatically determine the optimal segmentation order. Another characteristic is the application of the modified component-wise expectation maximization algorithm in Gath-Geva clustering which can avoid the drawbacks of the classical expectation maximization algorithm: the sensitivity to initialization and the need to avoid the boundary of the parameter space. The other characteristic is the improvement of numerical stability by integrating segmentation order selection into model parameter estimation procedure. The proposed algorithm has been experimentally tested on artificial and hydrometeorological time series. The obtained experimental results show the effectiveness of our proposed algorithm.