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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| Vydáno v: | Stochastic environmental research and risk assessment Ročník 26; číslo 1; s. 139 - 155 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Berlin/Heidelberg
Springer-Verlag
01.01.2012
Springer Nature B.V |
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| ISSN: | 1436-3240, 1436-3259 |
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| Abstract | 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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| AbstractList | 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. 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.[PUBLICATION ABSTRACT] |
| Author | Yin, Jianchuan Wang, Nini Liu, Xiaodong |
| Author_xml | – sequence: 1 givenname: Nini surname: Wang fullname: Wang, Nini email: wangnini2008@gmail.com organization: Research Center of Information and Control, Dalian University of Technology, Department of Mathematics, Dalian Maritime University – sequence: 2 givenname: Xiaodong surname: Liu fullname: Liu, Xiaodong organization: Research Center of Information and Control, Dalian University of Technology, Department of Mathematics, Dalian Maritime University – sequence: 3 givenname: Jianchuan surname: Yin fullname: Yin, Jianchuan organization: Navigation College, Dalian Maritime University, School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University |
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| Cites_doi | 10.1023/A:1024988512476 10.1109/TKDE.2004.17 10.1002/hyp.7064 10.1109/34.990138 10.1007/s00477-005-0013-6 10.1109/TPAMI.2010.44 10.1016/j.fss.2006.04.007 10.1109/TKDE.2010.161 10.1023/A:1007506220214 10.1109/34.192473 10.1111/j.1751-5823.2001.tb00456.x 10.1016/j.patrec.2008.06.013 10.1016/j.fss.2004.07.008 10.1109/TITB.2008.907984 10.1007/978-3-540-45231-7_26 10.1109/TNN.2010.2080319 10.1007/PL00013450 10.1016/j.patcog.2009.05.004 10.1007/s00477-009-0335-x 10.1109/TIP.2009.2039664 10.1002/hyp.7077 10.1007/s00477-007-0115-4 10.5194/npg-13-339-2006 10.1109/TNN.2006.882813 10.1007/s00477-006-0092-z 10.1016/j.patcog.2005.01.025 10.1007/s00477-006-0091-0 10.1007/s00477-003-0145-5 10.1109/TKDE.2008.29 |
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| Keywords | Gath–Geva (GG) clustering Minimum message length (MML) criterion Time series segmentation Segmentation order Expectation maximization (EM) algorithm |
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| References | AksoyHUnalNEGedikliALetter to the editorStoch Env Res Risk Assess20072144744910.1007/s00477-006-0091-0 FischDGruberTSickBSwiftrule: Mining comprehensible classification rules for time series analysisIEEE Trans Knowl Data Eng201123577478710.1109/TKDE.2010.161 KehagiasAFortinVTime series segmentation with shifting means hidden markov modelsNonlinear Process Geophys20061333935210.5194/npg-13-339-2006 GedikliAAksoyHUnalNESegmentation algorithm for long time series analysisStoch Env Res Risk Assess200822329130210.1007/s00477-007-0115-4 HubertPThe segmentation procedure as a tool for discrete modeling of hydrometeorological regimesStoch Env Res Risk Assess20001429730410.1007/PL00013450 LantermanADSchwarz, Wallace, and Rissanen intertwining themes in theories of model order estimationInt Stat Rev200169218521210.1111/j.1751-5823.2001.tb00456.x VernieuweHDe BaetsBVerhoestNECComparison of clustering algorithms in the identification of Takagi-Sugeno models: A hydrological case studyFuzzy Sets Syst20061572876289610.1016/j.fss.2006.04.007 AthanasiadisEICavourasDASpyridonosPPGlotsosDTKalatzisIKNikiforidisGCComplementary DNA microarray image processing based on the fuzzy gaussian mixture modelIEEE Trans Inf Technol Biomed200913441942510.1109/TITB.2008.907984 KehagiasAA hidden markov model segmentation procedure for hydrological and environmental time seriesStoch Env Res Risk Assess20041811713010.1007/s00477-003-0145-5 KehagiasANidelkouEPetridisVA dynamic programming segmentation procedure for hydrological and environmental time seriesStoch Env Res Risk Assess200520779410.1007/s00477-005-0013-6 Abonyi J, Feil B, Nemeth S, Arva P (2003) Fuzzy clustering based segmentation of time-series. 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| References_xml | – reference: AthanasiadisEICavourasDASpyridonosPPGlotsosDTKalatzisIKNikiforidisGCComplementary DNA microarray image processing based on the fuzzy gaussian mixture modelIEEE Trans Inf Technol Biomed200913441942510.1109/TITB.2008.907984 – reference: Celeux G, Chretien S, Forbes F, Mkhadri A (1999) A component-wise EM algorithm for mixtures. Technical report 3746, INRIA, France – reference: FischDGruberTSickBSwiftrule: Mining comprehensible classification rules for time series analysisIEEE Trans Knowl Data Eng201123577478710.1109/TKDE.2010.161 – reference: KehagiasAFortinVTime series segmentation with shifting means hidden markov modelsNonlinear Process Geophys20061333935210.5194/npg-13-339-2006 – reference: KehagiasAA hidden markov model segmentation procedure for hydrological and environmental time seriesStoch Env Res Risk Assess20041811713010.1007/s00477-003-0145-5 – reference: PovinelliRJohnsonMLindgrenAYeJTime series classification using Gaussian mixture models of reconstructed phase spacesIEEE Trans Knowl Data Eng200416677978310.1109/TKDE.2004.17 – reference: AksoyHUnalNEGedikliALetter to the editorStoch Env Res Risk Assess20072144744910.1007/s00477-006-0091-0 – reference: AksoyHUnalNEPektasAOSmoothed minima baseflow separation tool for perennial and intermittent streamsHydrol Process2008224467447610.1002/hyp.7077 – reference: GedikliAAksoyHUnalNEKehagiasAModified dynamic programming approach for offline segmentation of long hydrometeorological time seriesStoch Env Res Risk Assess20102454755710.1007/s00477-009-0335-x – reference: Hanlon B, Forbes C (2002) Model selection criteria for segmented time series from a bayesian approach to information compression. Working paper, Department of Econometrics and Statistics, Monash University, Melbourne, Australia – reference: BeefermanDBergerALaffertyJStatistical models for text segmentationMach Learn19993417721010.1023/A:1007506220214 – reference: Warren LiaoTClustering of time series data-a surveyPattern Recognit2005381857187410.1016/j.patcog.2005.01.025 – reference: KehagiasAPetridisVNidelkouEReply by the authors to the letter by Aksoy et alStoch Env Res Risk Assess20072145145510.1007/s00477-006-0092-z – reference: LantermanADSchwarz, Wallace, and Rissanen intertwining themes in theories of model order estimationInt Stat Rev200169218521210.1111/j.1751-5823.2001.tb00456.x – reference: NascimentoJCFigueiredoMMarquesJSTrajectory classification using switched dynamical hidden Markov modelsIEEE Trans Image Process20101951338134810.1109/TIP.2009.2039664 – reference: Abonyi J, Feil B, Nemeth S, Arva P (2003) Fuzzy clustering based segmentation of time-series. 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| SubjectTerms | 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 |
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| Title | Improved Gath–Geva clustering for fuzzy segmentation of hydrometeorological time series |
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