Results 21 to 30 of about 303,598 (207)
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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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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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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Clustering Time Series with Clipped Data [PDF]
Machine Learning, 2005 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Bagnall, Anthony J., Janacek, Gareth J.
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Exploring Dynamic Structures in Matrix-Valued Time Series via Principal Component Analysis
Axioms, 2023 Time-series data are widespread and have inspired numerous research works in machine learning and data analysis fields for the classification and clustering of temporal data. While there are several clustering methods for univariate time series and a few
Lynne Billard +2 more
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Time Series Clustering and Classification [PDF]
, 2019 Questo libro contiene informazioni ottenute da fonti autentiche e apprezzate. Sono stati compiuti sforzi ragionevoli per pubblicare dati e informazioni affidabili, ma l'autore e l'editore non possono assumersi la responsabilità€ della validità€ di tutti i materiali o delle conseguenze del loro utilizzo.
E. A. Maharaj +2 more
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Deep Convolutional Clustering-Based Time Series Anomaly Detection
Sensors, 2021 This paper presents a novel approach for anomaly detection in industrial processes. The system solely relies on unlabeled data and employs a 1D-convolutional neural network-based deep autoencoder architecture.
Gavneet Singh Chadha +3 more
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Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm [PDF]
, 2013 We live in an era of abundant data. This has necessitated the development of new and innovative statistical algorithms to get the most from experimental data.
Cooke, Emma J. +17 more
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On Clustering fMRI Time Series
NeuroImage, 1999 Analysis of fMRI time series is often performed by extracting one or more parameters for the individual voxels. Methods based, e.g., on various statistical tests are then used to yield parameters corresponding to probability of activation or activation strength.
Goutte, C +4 more
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Bayesian hierarchical clustering for microarray time series data with replicates and outlier measurements [PDF]
, 2011 Background Post-genomic molecular biology has resulted in an explosion of data, providing measurements for large numbers of genes, proteins and metabolites.
Cooke Emma J +14 more
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