Improved Gath–Geva clustering for fuzzy segmentation of hydrometeorological time series

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 segment...

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Bibliographic Details
Published in:Stochastic environmental research and risk assessment Vol. 26; no. 1; pp. 139 - 155
Main Authors: Wang, Nini, Liu, Xiaodong, Yin, Jianchuan
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer-Verlag 01.01.2012
Springer Nature B.V
Subjects:
Algorithms
Aquatic Pollution
Chemistry and Earth Sciences
Cluster analysis
Clustering
Computational Intelligence
Computer Science
Criteria
Earth and Environmental Science
Earth Sciences
Environment
Expectations
Fuzzy
Hydrology
Math. Appl. in Environmental Science
Mathematical models
Maximization
Original Paper
Physics
Probability Theory and Stochastic Processes
Segmentation
Statistics for Engineering
Time series
Waste Water Technology
Water Management
Water Pollution Control
Gath–Geva (GG) clustering
Minimum message length (MML) criterion
Time series segmentation
Segmentation order
Expectation maximization (EM) algorithm
ISSN:1436-3240, 1436-3259
Online Access:Get full text
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Summary: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.
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ISSN:1436-3240
1436-3259
DOI:10.1007/s00477-011-0542-0