Results 31 to 40 of about 227,005 (260)
Multi-Objective Optimisation for the Selection of Clusterings across Time
Engineering ProceedingsNowadays, time series data are ubiquitous, encompassing various domains like medicine, economics, energy, climate science and the Internet of Things. One crucial task in analysing these data is clustering, aiming to find patterns that indicate previously
Sergej Korlakov +3 more
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Time Series Clustering Method Based on Contrastive Learning [PDF]
Jisuanji kexueIt is difficult to intuitively define the similarity between time series by deep clustering methods which rely heavily on complex feature extraction networks and clustering algorithms.Contrastive learning can define the interval similarity of time series
YANG Bo, LUO Jiachen, SONG Yantao, WU Hongtao, PENG Furong
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JET: Fast Estimation of Hierarchical Time Series Clustering
Engineering ProceedingsClustering is an effective, unsupervised classification approach for time series analysis applications that suffer a natural lack of training data. One such application is the development of jet engines, which involves numerous test runs and failure ...
Phillip Wenig +2 more
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TSclust: An R Package for Time Series Clustering
Journal of Statistical Software, 2014 Time series clustering is an active research area with applications in a wide range of fields. One key component in cluster analysis is determining a proper dissimilarity measure between two data objects, and many criteria have been proposed in the ...
Pablo Montero, José A. Vilar
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Abridged Symbolic Representation of Time Series for Clustering
Acta Universitatis Lodziensis. Folia Oeconomica, 2019 In recent years a couple of methods aimed at time series symbolic representation have been introduced or developed. This activity is mainly justified by practical considerations such memory savings or fast data base searching.
Jerzy Korzeniewski
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Malaysia PM10 Air Quality Time Series Clustering Based on Dynamic Time Warping
Atmosphere, 2022 Air quality monitoring is important in the management of the environment and pollution. In this study, time series of PM10 from air quality monitoring stations in Malaysia were clustered based on similarity in terms of time series patterns.
Fatin Nur Afiqah Suris +4 more
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Consistent Algorithms for Clustering Time Series. [PDF]
J. Mach. Learn. Res., 2016 The problem of clustering is considered for the case where every point is a time series. The time series are either given in one batch (offline setting), or they are allowed to grow with time and new time series can be added along the way (online setting).
Khaleghi, Azadeh +3 more
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Interactive Time Series Clustering with COBRASTS [PDF]
, 2019 Time series are ubiquitous, resulting in substantial interest in time series data mining. Clustering is one of the most widely used techniques in this setting. Recent work has shown that time series clustering can benefit greatly from small amounts of supervision in the form of pairwise constraints.
Van Craenendonck, Toon +3 more
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Structural insights into an engineered feruloyl esterase with improved MHET degrading properties
FEBS Letters, EarlyView.A feruloyl esterase was engineered to mimic key features of MHETase, enhancing the degradation of PET oligomers. Structural and computational analysis reveal how a point mutation stabilizes the active site and reshapes the binding cleft, expading substrate scope.
Panagiota Karampa +5 more
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Incremental fuzzy C medoids clustering of time series data using dynamic time warping distance. [PDF]
PLoS ONE, 2018 Clustering time series data is of great significance since it could extract meaningful statistics and other characteristics. Especially in biomedical engineering, outstanding clustering algorithms for time series may help improve the health level of ...
Yongli Liu +4 more
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