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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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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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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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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Fuzzy clustering of time series gene expression data with cubic-spline [PDF]
, 2013 Data clustering techniques have been applied to ex- tract information from gene expression data for two decades. A large volume of novel clustering algorithms have been developed and achieved great success.
Ali, Akhtar, Wang, Yu, Angelova, Maia
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Time series clustering in large data sets
Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 2011 The clustering of time series is a widely researched area. There are many methods for dealing with this task. We are actually using the Self-organizing map (SOM) with the unsupervised learning algorithm for clustering of time series.
Jiří Fejfar, Jiří Šťastný
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INGARCH-based fuzzy clustering of count time series with a football application
Machine Learning with Applications, 2022 Although there are many contributions in the time series clustering literature, few studies still deal with count time series data. This paper aims to develop a fuzzy clustering procedure for count time series data.
Roy Cerqueti +4 more
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Review of Multivariate Time Series Clustering Algorithms [PDF]
Jisuanji kexue yu tansuoMultivariate time series (MTS) data, serving as a crucial basis for intelligent technologies across numerous domains, record the state changes of multiple variables in systems over time.
ZHENG Desheng, SUN Hanming, WANG Liyuan, DUAN Yaoxin, LI Xiaoyu
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Clustering time series applied to energy markets
Energy Informatics, 2019 In Germany and many other countries the energy market has been subject to significant changes. Instead of only a few large-scale producers that serve aggregated consumers, a shift towards regenerative energy sources is taking place.
Cornelia Krome, Jan Höft, Volker Sander
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A temporal precedence based clustering method for gene expression microarray data [PDF]
, 2010 Background: Time-course microarray experiments can produce useful data which can help in understanding the underlying dynamics of the system. Clustering is an important stage in microarray data analysis where the data is grouped together according to ...
Li Chang-Tsun +8 more
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