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Time series clustering of COVID-19 pandemic-related data [PDF]
Data Science and Management, 2023 The COVID-19 pandemic continues to impact daily life worldwide. It would be helpful and valuable if we could obtain valid information from the COVID-19 pandemic sequential data itself for characterizing the pandemic.
Zhixue Luo, Lin Zhang, Na Liu, Ye Wu
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Lag penalized weighted correlation for time series clustering [PDF]
BMC Bioinformatics, 2020 Background The similarity or distance measure used for clustering can generate intuitive and interpretable clusters when it is tailored to the unique characteristics of the data.
Thevaa Chandereng, Anthony Gitter
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A Review of Subsequence Time Series Clustering [PDF]
The Scientific World Journal, 2014 Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and ...
Seyedjamal Zolhavarieh +2 more
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Measuring Extremal Clustering in Time Series
Engineering Proceedings, 2023 The propensity of data to cluster at extreme values is important for risk assessment. For example, heavy rain over time leads to catastrophic floods. The extremal index is a measure of Extreme Values Theory that allows measurement of the degree of high ...
Marta Ferreira
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Clustering time series based on dependence structure. [PDF]
PLoS ONE, 2018 The clustering of time series has attracted growing research interest in recent years. The most popular clustering methods assume that the time series are only linearly dependent but this assumption usually fails in practice. To overcome this limitation,
Beibei Zhang, Baiguo An
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Efficient Time Series Clustering by Minimizing Dynamic Time Warping Utilization
IEEE Access, 2021 Dynamic Time Warping (DTW) is a widely used distance measurement in time series clustering. DTW distance is invariant to time series phase perturbations but has a quadratic complexity.
Borui Cai +4 more
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Satellite Image Time Series Clustering via Time Adaptive Optimal Transport
Remote Sensing, 2021 Satellite Image Time Series (SITS) have become more accessible in recent years and SITS analysis has attracted increasing research interest. Given that labeled SITS training samples are time and effort consuming to acquire, clustering or unsupervised ...
Zheng Zhang +3 more
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Journal of Statistical Theory and Applications (JSTA), 2018 One of data mining schemes in statistics is clustering panel data such as longitudinal data and time series data. Classical approaches to cluster such time dependent information do not properly count time dependencies among objects we are interested to ...
Doo Young Kim, Chris P. Tsokos
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A benchmark study on time series clustering
Machine Learning with Applications, 2020 This paper presents the first time series clustering benchmark utilizing all time series datasets currently available in the University of California Riverside (UCR) archive — the state of the art repository of time series data.
Ali Javed, Byung Suk Lee, Donna M. Rizzo
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Dynamic Matrix Clustering Method for Time Series Events
Jisuanji kexue yu tansuo, 2021 Time series events clustering is the basis of studying the classification of events and mining analysis. Most of the existing clustering methods directly aim at continuous events with time attribute and complex structure, but the transformation of ...
MA Ruiqiang, SONG Baoyan, DING Linlin, WANG Junlu
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