Results 41 to 50 of about 303,598 (207)

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, Qingzhang Shi, Jiawei Zhu, Jian Peng, Haifeng Li   +4 more
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Clustering of multivariate time-series data [PDF]

Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301), 2002
A new methodology for clustering multivariate time-series data is proposed. The methodology is based on calculation of the degree of similarity between multivariate time-series datasets using two similarity factors. One similarity factor is based on principal component analysis and the angles between the principal component subspaces while the other is
Ashish Singhal, Dale E. Seborg
openaire   +1 more source

Data-Adaptive Dynamic Time Warping-Based Multivariate Time Series Fuzzy Clustering

IEEE Access
Multivariate 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, Leiying Chen, Jian Shao, Ling Chen   +3 more
doaj   +1 more source

Multi-Objective Optimisation for the Selection of Clusterings across Time

Engineering Proceedings
Nowadays, 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, Gerhard Klassen, Luca T. Bauer, Stefan Conrad   +3 more
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Time Series Clustering Method Based on Contrastive Learning [PDF]

Jisuanji kexue
It 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
doaj   +1 more source

Temporal clustering by affinity propagation reveals transcriptional modules in Arabidopsis thaliana [PDF]

, 2009
Motivation: Identifying regulatory modules is an important task in the exploratory analysis of gene expression time series data. Clustering algorithms are often used for this purpose.
Buchanan-Wollaston, Vicky, Kiddle, Steven J, Denby, Katherine J, Denby, Katherine J., Mukherjee, Sach, Mead, Andrew, Windram, Oliver P F, Windram, Oliver P., Kiddle, Steven J., McHattie, Stuart, Mead, A. (Andrew), Beynon, Jim   +11 more
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JET: Fast Estimation of Hierarchical Time Series Clustering

Engineering Proceedings
Clustering 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, Mathias Höfgen, Thorsten Papenbrock   +2 more
doaj   +1 more source

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
doaj   +1 more source

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, Meert, Wannes, Dumancic, Sebastijan, Blockeel, Hendrik   +3 more
openaire   +1 more source

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
doaj   +1 more source