Results 21 to 30 of about 18,151,622 (163)
Using Permutations for Hierarchical Clustering of Time Series
Entropy, 2019 Two distances based on permutations are considered to measure the similarity of two time series according to their strength of dependency. The distance measures are used together with different linkages to get hierarchical clustering methods of time ...
Jose S. Cánovas +2 more
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Topic Network Analysis Based on Co-Occurrence Time Series Clustering
Mathematics, 2022 Traditional topic research divides similar topics into the same cluster according to clustering or classification from the perspective of users, which ignores the deep relationship within and between topics. In this paper, topic analysis is achieved from
Weibin Lin +4 more
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A framework for time-series clustering-as-a-aservice. [PDF]
, 2022 LAUREA MAGISTRALEIl clustering di serie temporali ha effettivamente fornito informazioni utili in diversi domini applicativi. C'è un crescente interesse per il raggruppamento di serie temporali come parte dello sforzo nella ricerca di data mining ...
GUADARRAMA RAMIREZ, URIEL
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Time Series Clustering with Topological and Geometric Mixed Distance
Mathematics, 2021 Time series clustering is an essential ingredient of unsupervised learning techniques. It provides an understanding of the intrinsic properties of data upon exploiting similarity measures.
Yunsheng Zhang +4 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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Equivalence partition based morphological similarity clustering for large-scale time series
Scientific Reports, 2023 Data clustering belongs to the category of unsupervised learning and plays an important role in the dynamic systems and big data. The clustering problem of sampled time-series data is undoubtedly much more challenging than that of repeatable sampling ...
Shaolin Hu
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Data-Adaptive Dynamic Time Warping-Based Multivariate Time Series Fuzzy Clustering
IEEE AccessMultivariate time series (MTS) clustering has become a critical research area. Current methods typically rely on space projection or representation learning for clustering but tend to overlook the significance and contribution of MTS dimensions, leading ...
Qinglin Cai +3 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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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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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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