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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