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Time-series clustering – A decade review
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“Methods for clustering time-series data are commonly divided into three classes (Aghabozorgi et al., 2015): modelbased, feature-based and shape-based approaches.”
From Ink and algorithm: Exploring temporal dynamics in generative AI ‐assisted writing (2026) · cites this paper as Aghabozorgi et al., 2015
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Hubert, L., & Arabie, P. (1985) · Journal of Classification · doi:10.1007/bf01908075 · Cited by 8,219
Dynamic Programming Algorithm Optimization for Spoken Word Recognition
Sakoe, H., & Chiba, S. (1978) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1978.1163055 · Cited by 6,727
A massively parallel architecture for a self-organizing neural pattern recognition machine
Carpenter, G. A., & Grossberg, S. (1987) · Computer Vision, Graphics, and Image Processing · doi:10.1016/s0734-189x(87)80014-2 · Cited by 2,525
BIRCH: an efficient data clustering method for very large databases
Zhang, T., Ramakrishnan, R., & Livny, M. (1996) · ACM SIGMOD Record · doi:10.1145/235968.233324 · Cited by 2,097
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Items 701–800 of 1,000
Financial Time Series Clustering
Gupta, K., & Chatterjee, N. (2017) · doi:10.1007/978-3-319-63645-0_16 · Cited by 7
Translation Registration Algorithm for Multi - source Time Series Data Based on the Sliding Window
Lu, H.-M., Di, T.-Y., Song, J., Sun, H., Han, X., & Liu, G. (2016) · doi:10.25103/jestr.095.05 · Cited by 7
“It can predict future development trends through time series analysis and data mining [6], [7].”
BackgroundAnomaly Detection in Smart Industrial Machinery Through Hidden Markov Models and Autoencoders
Sorostinean, R., Burghelea, Z., & Gellért, Á. (2024) · IEEE Access · doi:10.1109/access.2024.3400970 · Cited by 14
“As previously mentioned, the temporal information is not taken inherently into account in [10] and [11].”
BackgroundThe time series clustering of stock price in LQ45 index and its financial performance analysis
Afrimayani, & Devianto, D. (2021) · Journal of Physics, Conference Series · doi:10.1088/1742-6596/1943/1/012129 · Cited by 7
A Comprehensive Review on Unsupervised Feature Selection Algorithms
Pandit, A. A., Pimpale, B., & Dubey, S. (2019) · Algorithms for intelligent systems · doi:10.1007/978-981-15-0633-8_24 · Cited by 7
Cluster Analysis of Microarray Data
Franco, M., & Vivo, J.-M. (2019) · Methods in molecular biology · doi:10.1007/978-1-4939-9442-7_7 · Cited by 21
Fine-grained complexity analysis of some combinatorial data science problems
Froese, V. (2018) · doi:10.14279/depositonce-7123 · Cited by 7
Fuzzy clustering of time series based on trend feature information granulation
Yu, B., & Wu, C. (2025) · Fuzzy Sets Syst. · doi:10.1016/j.fss.2025.109522 · Cited by 6
Efrat, N., Dubinin, V., Baram, S., & Paz-Kagan, T. (2025) · Agricultural Water Management · doi:10.1016/j.agwat.2025.109868 · Cited by 6
Exploring Economic Profit of EV Aggregators in Supplying Flexibility Services through V2G
Caro, F. D., Graber, G., Calderaro, V., Vaccaro, A., & Galdi, V. (2025) · Sustainable Energy, Grids and Networks · doi:10.1016/j.segan.2025.101851 · Cited by 6
Suschnigg, J., Mutlu, B., Burgholzer, M., Bauer, M., & Schreck, T. (2025) · International Conference on Intelligent User Interfaces · doi:10.1145/3708359.3712076 · Cited by 6
“This feature-extracted time series representation is necessary because processing the raw data is computationally expensive [1].”
Subspace time series clustering of meteocean data to support ocean and coastal hydrodynamic modeling
Tan, W.-K., Stocchino, A., & Cai, Z.-Y. (2024) · Ocean Engineering · doi:10.1016/j.oceaneng.2024.119417 · Cited by 6
Quantitative Classification of Spring Discharge Patterns: A Cluster Analysis Approach
Seelig, M., Seelig, S., Vremec, M., Wagner, T., Brielmann, H., Eybl, J., & Winkler, G. (2024) · Hydrological Processes · doi:10.1002/hyp.15326 · Cited by 6
“This aggregation is carried out employing the k - means algorithm, yielding a set of clusters that is efficient in the sense of within-class variance (Aghabozorgi, Seyed Shirkhorshidi, and Ying Wah 2015; MacQueen 1967).”
Sari, K., & Balamane, W. (2024) · Energy · doi:10.1016/j.energy.2024.133516 · Cited by 6
Model-Based Clustering of RF-EMF Monitoring Data to Analyze Time Variability
Pasquino, N., Solmonte, N., Djuric, N., Kljajić, D., & Djuric, S. (2024) · IEEE International Workshop on Measurement and Networking · doi:10.1109/mn60932.2024.10615478 · Cited by 6
“Time-series clustering algorithms are classified into three categories [28]: i) time point clustering, ii) subsequence clustering, and iii) whole time-series clustering.”
MethodTime series clustering of dynamical systems via deterministic learning
Sun, C., Wu, W., Zhang, Z., Li, Z., Ji, B., & Wang, C. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02062-7 · Cited by 6
Joint Problems in Learning Multiple Dynamical Systems
Niu, M., He, X., Rysavý, P., Zhou, Q., & Mareček, J. (2023) · arXiv.org · doi:10.48550/arxiv.2311.02181 · Cited by 6
“The problem of clustering of time series is relevant in many fields, including [1], [63] applications in Bioinformatics, Multimedia, Robotics, Climate, and Finance.”
Scientific creativity patterns in scholars’ academic careers: Evidence from PubMed
Ao, W., Lyu, D., Ruan, X., Li, J., & Cheng, Y. (2023) · Journal of Informetrics · doi:10.1016/j.joi.2023.101463 · Cited by 9
The impact of window size on univariate time series forecasting using machine learning
Freitas, J., Ponte, C., Bomfim, R., & Caminha, C. (2023) · Anais do XI Symposium on Knowledge Discovery, Mining and Learning (KDMiLe 2023) · doi:10.5753/kdmile.2023.232916 · Cited by 6
“2018], clusterização [Aghabozorgi et al. 2015], classificação [Fawaz et al.”
Cyber Threat Assessment in Monitoring Turnout Railway Systems
Abdellaoui, S., Dumitrescu, E., Escudero, C., & Zamaĭ, E. (2023) · IEEE International Conference on Emerging Technologies and Factory Automation · doi:10.1109/etfa54631.2023.10275401 · Cited by 6
“Time-series clustering approaches are divided into 3 categories [37]: model-based approaches, feature-based approaches and shape-based approaches.”
BackgroundSubha, S., & Sathiaseelan, J. G. R. (2023) · SN Computer Science · doi:10.1007/s42979-023-01890-2 · Cited by 8
“Ci is the ith Cluster where D = k i=1 Ci and Ci ∩ Cj = ∅fori ≠ j [23] This paper proposes the MWSOM clustering algorithm to cluster time series labeled data.”
BackgroundClustering multivariate time series using energy distance
Davis, R., Fernandes, L., & Fokianos, K. (2023) · Journal of Time Series Analysis · doi:10.1111/jtsa.12688 · Cited by 6
“Various dissimilarity measures for clustering time series are catalogued in Liao (2005); Fu (2011); Montero and Vilar (2014); Aghabozorgi et al. (2015); Maharaj et al.”
BackgroundMethodInfluentialCausal Temporal Graph Convolutional Neural Networks (CTGCN)
Langbridge, A., O'Donncha, F., Ba, A., Lorenzi, F., Lohse, C., & Ploennigs, J. (2023) · arXiv.org · doi:10.48550/arxiv.2303.09634 · Cited by 6
“It has more recently been popularised as a method for the unsupervised classification of large temporal data in applications from finance to astronomy [1].”
MethodMeasurement-based coherency detection through Monte Carlo Consensus Clustering
Caro, F. D., Pepiciello, A., Milano, F., & Vaccaro, A. (2023) · Electric power systems research · doi:10.1016/j.epsr.2022.109075 · Cited by 6
Where forest may not return in the western United States
Wickham, J., Neale, A., Riitters, K., Nash, M., Dewitz, J., Jin, S., van Fossen, M., & Rosenbaum, D. (2023) · Ecological Indicators · doi:10.1016/j.ecolind.2022.109756 · Cited by 6
MATSA: An MRAM-Based Energy-Efficient Accelerator for Time Series Analysis
Fernández, I., Giannoula, C., Manglik, A., Quislant, R., Ghiasi, N. M., Gómez-Luna, J., Gutiérrez, E., Plata, O., & Mutlu, O. (2022) · IEEE Access · doi:10.1109/access.2024.3373311 · Cited by 6
Describing a complex primary health care population to support future decision support initiatives
Kueper, J. K., Rayner, J., Zwarenstein, M., & Lizotte, D. J. (2022) · International Journal for Population Data Science · doi:10.23889/ijpds.v7i1.1756 · Cited by 4
“To investigate frequency of care in terms of magnitude and shape (changes in magnitude across care histories), we performed time series clustering (unsupervised machine learning technique) with the K-Medoids algorithm and dynamic time warping distance metric [40] for 1) shortterm clients with 2–3 observation years and…”
Highly variable basking shark (Cetorhinus maximus) diving behavior in the lower Bay of Fundy, Canada
Siders, Z. A., Westgate, A. J., Bell, K. R., & Koopman, H. N. (2022) · Frontiers in Marine Science · doi:10.3389/fmars.2022.976857 · Cited by 7
“We used dynamic time warping (DTW), a common time-series comparison technique (Senin, 2008; Aghabozorgi et al., 2015), that determines a metric of distance between two time series of unequal length.”
MethodSENSOR: Data-driven Construction of Sketch-based Visual Query Interfaces for Time Series Data
Yan, L., Xu, N., Li, G., Bhowmick, S., Choi, B., & Xu, J.-L. (2022) · Proceedings of the VLDB Endowment · doi:10.14778/3554821.3554866 · Cited by 6
“Even if a time series dataset does not have such labels, any state-of-the-art time series clustering technique [1] can be used to generate them first ( i.e., each cluster is one class).”
Sirin, S. M., Uz, D., & Sevindik, I. (2022) · Energy Economics · doi:10.1016/j.eneco.2022.106169 · Cited by 6
Machine learning for lumbar and pelvis kinematics clustering
Higgins, S., Dutta, S., & Kakar, R. (2022) · bioRxiv · doi:10.1080/10255842.2023.2241593 · Cited by 6
“DTW DTW is a shape-based distance metric that measures the similarity between two time-series, which may vary in speed (Aghabozorgi et al. 2015).”
InfluentialA Review of Open Source Software Tools for Time Series Analysis
Faniband, Y. P., Ishak, I., & Sait, S. (2022) · arXiv.org · doi:10.48550/arxiv.2203.05195 · Cited by 6
“…et al. [2016] Panigrahi and Behera [2020] Tealab [2018] ), Classification ( TSK2 ) (Abanda et al. [2019] Bagnall et al. [2017]), Clustering ( TSK3 ) (Aghabozorgi et al. [2015]), Anomaly Detection ( TSK4 ) (Wu [2016], Cook et al. [2019], Ayadi et al. [2017] , Segmentation (Summarization) (TSK5),…”
Spatio-temporal clustering: Neighbourhoods based on median seasonal entropy
Reina, M. (2021) · doi:10.1016/j.spasta.2021.100535 · Cited by 6
Rojas-Valenzuela, I., Valenzuela, O., Delgado-Márquez, E., & Rojas, F. (2021) · Engineering Proceedings · doi:10.3390/engproc2021005053 · Cited by 6
“Clustering of time-series sequences has received noteworthy attention [11,12], not only as a formidable exploratory method and powerful tool for discovering patters, but also as a pre-processing step or subroutine for other tasks [13].”
BackgroundMethodClustering Data-Driven Local Control Schemes in Active Distribution Grids
Karagiannopoulos, S., Valverde, G., Aristidou, P., & Hug, G. (2021) · IEEE Systems Journal · doi:10.1109/jsyst.2020.3004277 · Cited by 6
“The interested reader is referred to [34] for a comprehensive review of time-series clustering algorithms, which organizes recent works in terms of the aforementioned types, computational complexity, applications, and evaluation measures.”
BackgroundMultivariate spatio-temporal analysis of the global COVID-19 pandemicPreprint
Xiang, W., & Swallow, B. (2021) · medRxiv · doi:10.1101/2021.02.08.21251339 · Cited by 6
Hillen, M., Legrand, S., Dirkx, Y., Janssens, K., van der Snickt, G., Caen, J., & Steenackers, G. (2020) · Applied Sciences · doi:10.3390/app10124255 · Cited by 6
Comprehensive Analysis of Time Series Forecasting Using Neural Networks
Tadayon, M., & Iwashita, Y. (2020) · arXiv.org · doi:10.48550/arxiv.2001.09547 · Cited by 6
“In [10] and [11], the authors performed the comprehensive surveys of the time series clustering methods.”
BackgroundMethodKamalzadeh, H., Sobhan, S. N., Boskabadi, A., Hatami, M., & Gharehyakheh, A. (2019) · RePEc: Research Papers in Economics · doi:10.48550/arxiv.1912.02373 · Cited by 8
“Although this paper is basically dealing with lots of time series as the input data, and lots of preprocessing techniques are developed and available in the relative literature [46]–[53], digging into these techniques and implementing them for such short time series as in this paper are avoided due to short lengths of…”
MethodHigh Dimensional Data Clustering by means of Distributed Dirichlet Process Mixture Models
Meguelati, K., Fontez, B., Hilgert, N., & Masseglia, F. (2019) · 2019 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata47090.2019.9006065 · Cited by 6
“For time series clustering, three categories are defined by [9]: “Whole time-series clustering”, “subsequence clustering”, and “time point clustering”.”
An average-compress algorithm for the sample mean problem under dynamic time warping
Jain, B. J., Froese, V., & Schultz, D. (2023) · Journal of Global Optimization · doi:10.1007/s10898-023-01294-9 · Cited by 6
“To account for temporal variations in proximity-based time series mining, the dynamic time warping (DTW) distance is often the preferred choice of proximity measure [1, 3, 4].”
BackgroundAriff, N. M., Bakar, M. A. A., Mahbar, S. F. S., & Nadzir, M. M. (2019) · Malaysian Journal of Science · doi:10.22452/mjs.sp2019no2.8 · Cited by 6
“85 recommendation in many field of studies such as in climate, energy, environment, finance and medicine (Aghabozorgi et al., 2015; Rani & Sikka, 2012).”
BackgroundInfluentialLinear Dynamics: Clustering without identification
Hsu, C. C.-Y., Hardt, M., & Hardt, M. (2019) · International Conference on Artificial Intelligence and Statistics · doi:10.48550/arxiv.1908.01039 · Cited by 6
“Time-series clustering has been extensively studied in many areas including biology, climatology, energy consumption, finance, medicine, robotics, and voice recognition [20, 21].”
BackgroundGray, D. (2019) · Urban studies · doi:10.1177/0042098019839886 · Cited by 6
“Time series analysis of co-movement is beset by problems of noise, amplitude, delays, discontinuities and temporal drift (Aghabozorgi et al., 2015).”
BackgroundInfluentialA Research Roadmap of Big Data Clustering Algorithms for Future Internet of Things
Bangui, H., Ge, M.-Z., & Buhnova, B. (2019) · International Journal of Organizational and Collective Intelligence · doi:10.4018/ijoci.2019040102 · Cited by 6
Navarrete-López, C., Herrera, M., Brentan, B., Luvizotto, E., & Izquierdo, J. (2019) · Water · doi:10.3390/w11020246 · Cited by 7
“There are several alternative ways to approach distances or (dis)similarities between time series [71].”
BackgroundLi, N., Zhang, C., Xie, W.-X., & Li, Y. (2018) · International journal of computer integrated manufacturing (Print) · doi:10.1080/0951192x.2018.1509129 · Cited by 6
A Smart City Dashboard for Combining and Analysing Multi-source Data Streams
Gledson, A., Dhafari, T. B., Paton, N. W., & Keane, J. A. (2018) · IEEE International Conference on High Performance Computing and Communications; IEEE International Conference on Smart City; IEEE International Conference on Data Science and Systems (HPCC/SmartCity/DSS) · doi:10.1109/hpcc/smartcity/dss.2018.00226 · Cited by 7
“Applying clustering on time-series data, where the clustering is based on stream features such as time-periods and/or locations, has the potential to identify similar patterns occurring at multiple times [32].”
MethodFeng, C., Cui, M., Hodge, B., Lu, S.-Y., Hamann, H., & Zhang, J. (2018) · arXiv.org · doi:10.48550/arxiv.1805.04193 · Cited by 6
Lauria, M., Persico, M., Dordevic, N., Cominetti, O., Matone, A., Hosking, J., Jeffery, A., Pinkney, J., Silva, L. D., Priami, C., et al. (2018) · Scientific Reports · doi:10.1038/s41598-017-19059-2 · Cited by 6
“Clustering of time series is extensively used in different areas of scientific research, and this is reflected in the abundance of different approaches that have been proposed and by the diversity of the respective sources 9 .”
Bidirectional Piecewise Linear Representation of Time Series and its Application in Clustering
Shi, W., Karastoyanova, D., Huang, Y.-M., & Zhang, G.-B. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3318728 · Cited by 6
“Therefore, traditional distance measures, such as Euclidean distance [33], [34] and Hausdorff distance [35], are not suitable for our method.”
MethodImpact of Data Quality in Real-Time Big Data Systems
Merino, J., Xie, X., Parlikad, A., Lewis, I., & McFarlane, D. (2020) · ER Forum/Posters/Demos · doi:10.17863/cam.59426 · Cited by 6
“For instance, data miners focus on accuracy and outliers removal as the most important factors that define data quality [28], [29].”
BackgroundTime-series clustering: A benchmark study on energy data with insights into demand response
Ahir, R. K., Delinchant, B., & Easwaran, A. (2026) · Engineering applications of artificial intelligence · doi:10.1016/j.engappai.2025.112892 · Cited by 5
Plein, M., Feigel, G., Zeeman, M., Dormann, C. F., & Christen, A. (2025) · Urban Climate · doi:10.1016/j.uclim.2025.102496 · Cited by 6
Yuval, Levi, Y., Khain, P., & Broday, D. (2025) · Science of the Total Environment · doi:10.1016/j.scitotenv.2025.180051 · Cited by 5
Time Series Information Visualization - A Review of Approaches and Tools
Ortigossa, E. S., Dias, F. F., do Nascimento, D. C., & Nonato, L. G. (2025) · IEEE Access · doi:10.1109/access.2025.3609404 · Cited by 6
“…data analysis, modeling, and visualization techniques is also extensive, providing designers with valuable content on mining [63], clustering [4], and aggregation [143] concepts and approaches that can be applied to the development pipeline of visualization tools devoted to exploring time…”
A comprehensive review of clustering methods applications in electric mobility
Forte, M., Guzmán, C. P., Pereira, L., & Morais, H. (2025) · Transport Engineer · doi:10.1016/j.treng.2025.100360 · Cited by 5
Lee, K., Yun, S.-J., & Park, S. (2025) · Environment, Development and Sustainability · doi:10.1007/s10668-025-06223-2 · Cited by 5
Runtime Anomaly Detection for Drones: An Integrated Rule-Mining and Unsupervised-Learning Approach
Tan, I., Minn, W., Poskitt, C. M., Shar, L. K., & Jiang, L. (2025) · IEEE International Conference on Engineering of Complex Computer Systems · doi:10.1007/978-3-032-00828-2_1 · Cited by 5
“[5] and Alqahtani et. al.”
Maximum information extraction via clustering and minimization of Shannon entropy
Becchi, M., & Pavan, G. M. (2025) · Machine Learning: Science and Technology · doi:10.1088/2632-2153/ae2dbb · Cited by 5
“Among the available algorithms for single point time-series analysis [32], here we use Onion clustering [31], an unsupervised method that identifies all statistically relevant micro-domains (including hidden ones) that can be resolved in a time-series of length τ as a function of ∆ t , as well as…”
An algorithm for improving lower bounds in dynamic time warping
Luo, Y., Fang, X., Ke, W., Lam, C.-T., Im, S., & Paquete, L. (2025) · Expert systems with applications · doi:10.1016/j.eswa.2025.127405 · Cited by 5
Identification and investigation of cruising speeds from cycling GPS data
Berjisian, E., & Bigazzi, A. (2025) · Transportation · doi:10.1007/s11116-025-10595-9 · Cited by 5
“Clustering time series data is more demanding than non-ordered data because it requires consideration of sequential patterns, and only a few clustering algorithms account for temporal dependency in multivariate data (Aghabozorgi et al. 2015; Hallac et al. 2017).”
Clustering-based demand forecasting with an application to immunoglobulin products
Rahimi, Z., Li, N., Down, D. G., & Arnold, D. M. (2025) · Operations Research Data Analytics and Logistics · doi:10.1016/j.ordal.2025.200469 · Cited by 4
In-Database Time Series Clustering
Su, Y.-X., Liang, K. Y., & Song, S.-X. (2025) · Proc. ACM Manag. Data · doi:10.1145/3709696 · Cited by 5
“Time series clustering strongly relies on series similarity measures [8].”
Exploring the diverse world of SAX-based methodologies
Pappa, L., Karvelis, P. S., & Stylios, C. (2024) · Data mining and knowledge discovery · doi:10.1007/s10618-024-01075-2 · Cited by 5
Gaussian-based plug load profile prediction in non-residential buildings archetype
Dabirian, S., Miller, C., Adli, A., & Eicker, U. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123970 · Cited by 5
Spatiotemporal variations and influencing factors of heatwaves in Chengdu, China
Li, Y.-X., Luo, X., Wang, M.-H., Di, B., Li, Y.-B., Tan, C.-P., & Pan, Y. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102924 · Cited by 5
Model-based clustering of time-dependent observations with common structural changes
Corradin, R., Danese, L., KhudaBukhsh, W. R., & Ongaro, A. (2024) · Statistics and computing · doi:10.1007/s11222-025-10756-x · Cited by 5
“For a comprehensive review of different clustering techniques for time series see Aghabozorgi et al. (2015).”
Clustering Method Comparison for Rural Occupant’s Behavior Based on Building Time-Series Energy Data
Liu, X.-D., Zhang, S.-M., Wang, X.-H., Wu, R., Yang, J.-Q., Zhang, H., Wu, J.-N., & Li, Z.-X. (2024) · Buildings · doi:10.3390/buildings14082491 · Cited by 5
Naser, A. Z., & Naser, M. Z. (2024) · Computers & Industrial Engineering · doi:10.1016/j.cie.2024.110812 · Cited by 4
Smartphone-based markers of social connectivity in schizophrenia and bipolar disorder
Valeri, L., Cai, X., Rahimi-Eichi, H., Liebenthal, E., Rauch, S. L., Öngür, D., Schutt, R. K., Dixon, L. B., Onnela, J.-P. “., & Baker, J. (2024) · NPP—Digital Psychiatry and Neuroscience · doi:10.1038/s44277-024-00013-w · Cited by 8
“We use a hierarchical clustering procedure in the primary analysis and the partitional clustering algorithm [17] in the secondary analysis to evaluate the sensitivity of results to the choice of clustering algorithm.”
Time series clustering using fragmented autocorrelations
Albino, A., Caiado, J., & Crato, N. (2024) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2024.129981 · Cited by 5
Persistent homology of featured time series data and its applications
Heo, E., & Jung, J.-H. (2024) · AIMS Mathematics · doi:10.3934/math.20241315 · Cited by 7
“Time series data analysis has been a long-standing field, including anomaly detection [1, 2], forecasting [3, 4, 5, 6], classification [7, 8, 9, 10], and clustering [11, 12, 13].”
Yokoi, R., Motoshita, M., Matsuda, T., & Itsubo, N. (2024) · Environmental Science and Technology · doi:10.1021/acs.est.4c00100 · Cited by 5
The Impact of Relocation Patterns on Psychological Stress
Deng, C. Z., Lee, H.-C., Chen, L. A., & Chen, S. (2024) · Psychological Science · doi:10.1177/09567976241239915 · Cited by 6
“DTW is an algorithm used for classification on the basis of pattern similarity (Aghabozorgi et al., 2015).”
MethodAccoroni, S., Neri, F., Ubaldi, M., Romagnoli, T., & Totti, C. (2024) · Phycologia · doi:10.1080/00318884.2024.2318819 · Cited by 6
“2013, Aghabozorgi et al .”
Profiling and Modeling of Power Characteristics of Leadership-Scale HPC System Workloads
Karimi, A. M., Sattar, N., Shin, W., & Wang, F.-Y. (2024) · arXiv.org · doi:10.48550/arxiv.2402.00729 · Cited by 5
“There are two categories for timeseries clustering algorithms [36]–[39]: raw-data-based or shape-based [40]–[42], and feature-based [43]–[45], and our work falls into the latter one.”
BackgroundAL-Jumaili, A. H. A., Muniyandi, R. C., Hasan, M. K., Singh, M. J., Paw, J. K. S., & Al-Jumaily, A. (2024) · Intelligent Data Analysis · doi:10.3233/ida-230573 · Cited by 5
“The literature [28] suggested a hybrid two-step strategy for creating several sub clusters before combining those using K-Medoids.”
Circular Arc Length-Based Kernel Matrix For Protein Sequence Classification
Murad, T., Ali, S., Chourasia, P., Mansoor, H., & Patterson, M. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386477 · Cited by 5
“Sequence data require specialized distance metrics like Hamming distance [19], [20], Jaccard similarity [21], or edit distance (or Levenshtein distance measures) [21], [22], which consider the data’s discrete and sequential nature.”
Early Detection of Mental and Behavioral Health Issues from High-School Academic Performance
Porello, D., Buy, U., Boerke, K. W., & Keller, M. (2023) · International Conference on Machine Learning and Applications · doi:10.1109/icmla58977.2023.00059 · Cited by 5
“A decade review” [5] mainly refers to general approaches that solve the problem of time series clustering, while only a few works deal with academic performance data.”
BackgroundFeature-based and shape-match classifications of animal population time series
Wang, G.-M., & Shan, X. (2023) · Ecological Informatics · doi:10.1016/j.ecoinf.2023.102396 · Cited by 5
Evaluating multivariate time-series clustering using simulated ecological momentary assessment data
Ntekouli, M., Spanakis, G., Waldorp, L., & Roefs, A. (2023) · Machine Learning with Applications · doi:10.1016/j.mlwa.2023.100512 · Cited by 2
Gromov, V. A., & Baranov, P. S. (2023) · Complex · doi:10.1155/2023/6689371 · Cited by 5
Symmetrical SyncMap for imbalanced general chunking problems
Zhang, H., & Vargas, D. V. (2023) · Physica D Nonlinear Phenomena · doi:10.1016/j.physd.2023.133923 · Cited by 2
“Time series data is defined as a sequence of continuous, real-valued elements, usually with high dimensionality and large data size [18].”
BackgroundAn integrated approach for knowledge extraction and analysis in collaborative knowledge construction
Zhang, N., & Ouyang, F. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00414-5 · Cited by 9
“Euclidean distance is a commonly used definition of distance that calculates the arithmetic mean value of each time slice (Aghabozorgi et al., 2015).”
BackgroundMachine learning in sensor identification for industrial systems
Weber, L., & Lenz, R. (2023) · it - Information Technology · doi:10.1515/itit-2023-0051 · Cited by 5
Fast dynamic time warping and clustering in C++
Kumtepeli, V., Perriment, R., & Howey, D. (2023) · Journal of Open Source Software · doi:10.21105/joss.06881 · Cited by 5
Unsupervised classification of plethysmography signals with advanced visual representations
Germain, T., Truong, C., Oudre, L., & Krejci, E. (2023) · Frontiers in Physiology · doi:10.3389/fphys.2023.1154328 · Cited by 5
“Indeed, the quality of each cluster is highly dependent on the quality of its centroid (Aghabozorgi et al., 2015).”
BackgroundVorpe, K., Hessinger, S., Poth, R., & Miljkovic, T. (2023) · Journal of Applied Statistics · doi:10.1080/02664763.2023.2192445 · Cited by 5
“A few commonly used clustering approaches are based on hierarchical and partitional methods [3] that differ based on the way they handle the observations and how underlying groupings are determined.”
MethodOu, P., Lai, T., Huang, S., Chen, W., & Weng, D. (2023) · Remote Sensing · doi:10.3390/rs15071744 · Cited by 5
Qin, X., Zhao, L.-L., Yang, J., Li, P., Wu, B.-F., Sun, K.-M., & Xu, Y.-B. (2022) · Remote Sensing · doi:10.3390/rs14236073 · Cited by 5
Singley, J., Singha, K., Gooseff, M., González‐Pinzón, R., Covino, T., Ward, A., Dorley, J., & Hinckley, E.-L. S. (2022) · Hydrological Processes · doi:10.1002/hyp.14713 · Cited by 5
“…distance metric that retains the physical meaning of the time-series values (no unit conversion) and are sensitive to both scaling and synchronicity in structure amongst time series, unlike other metrics used for time-series comparisons (e.g., Aghabozorgi et al., 2015; Mueen & Keogh, 2016).”
MethodOn computing exact means of time series using the move-split-merge metric
Holznigenkemper, J., Komusiewicz, C., & Seeger, B. (2023) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00908-2 · Cited by 2
“Since clustering methods based on partitioning rely on cluster prototype determination (Aghabozorgi et al. 2015), it is necessary to compute an accurate mean.”
BackgroundMethodTemporal Clustering of the Causes of Death for Mortality Modelling
Bett, N., Kasozi, J., & Ruturwa, D. (2022) · Risks · doi:10.3390/risks10050099 · Cited by 5
“According to Aghabozorgi et al. (2015), clustering applications in the field of timestamped data are based on sequential data measurements taken across a period from the same source and are used to track change over time, i.e., Dynamic Time Warping, DTW (Lee et al. 2020; Sakoe 1971).”
MethodMachine Learning Dynamic Switching Approach to Forecasting in the Presence of Structural Breaks
Pinto, J. M., & Castle, J. L. (2022) · Journal of Business Cycle Research · doi:10.1007/s41549-022-00066-w · Cited by 5
“The definition of time series clustering is well stated in Aghabozorgi et al. (2015) and is defined as follows: Definition 1 Time Series Clustering: given a dataset of n time series data D = F 1 , F 2 , … , F n the process of unsupervised partitioning of D in C = C 1 , C 2 , … , C k , in such way…”
BackgroundMethodInfluentialSenger, D., Johannsen, C., & Kluss, T. (2022) · IEEE Conference on Technologies for Sustainability · doi:10.1109/sustech53338.2022.9794223 · Cited by 5
Trajectory Clustering Performance Evaluation: If we know the answer, it's not clustering
Rezaie, M., & Saunier, N. (2021) · arXiv.org · doi:10.48550/arxiv.2112.01570 · Cited by 5
“Among these similarity measures, LCSS and DTW are very popular among researchers for their ability to compare trajectories with different lengths [1, 29, 35, 7].”
BackgroundMethodHexadecimal Aggregate Approximation Representation and Classification of Time Series Data
He, Z., Zhang, C., Ma, X.-G., & Liu, G. (2021) · Algorithms · doi:10.3390/a14120353 · Cited by 5
“The taxonomy of time series representations includes four types [10]: data-adaptive, non-data”
BackgroundMethodA Comparative Analysis of Machine Learning Models for the Prediction of Insurance Uptake in Kenya
Yego, N., Kasozi, J., & Nkurunziza, J. (2021) · International Conference on Data Technologies and Applications · doi:10.3390/data6110116 · Cited by 5
“…and insurance ranging from the application to modeling of life insurance companies [10], as well as in export credit finance claims prediction [11], the cost-sensitive type of credit scoring [12], time series related data including stock prices [13,14], and even in product sentiment analysis [15].”
BackgroundHierarchical Multiresolution Representation of Streaming Time Series
Manojlović, I., Svenda, G., Erdeljan, A., Gavrić, M., & Čapko, D. (2021) · Big Data Research · doi:10.1016/j.bdr.2021.100256 · Cited by 5
Metadata
- Authors
- Aghabozorgi, S., Seyed Shirkhorshidi, A., & Ying Wah, T.
- Affiliations
- University of Malaya (MY)
- ORCID
- Aghabozorgi, Saeed
- Venue
- Information Systems
- Publisher
- Elsevier BV
- Published
- 2015-10
- Volume
- 53
- Pages
- 16-38
- ISSN
- 0306-4379 (ISSN-L)
- OpenAlex
- W1894414046
- Cited by
- 1,826
- References
- 280
- Citations by year
- 2026: 118 · 2025: 195 · 2024: 210 · 2023: 252 · 2022: 214 · 2021: 225 · 2020: 215 · 2019: 190 · 2018: 112 · 2017: 56 · 2016: 31 · 2015: 6
- FWCI
- 83.19
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Information Systems (publishedVersion)
- Funders
- University of Malaya Research (RP0061-13ICT)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Physical Sciences › Computer Science › Information Systems › Data Management and Algorithms
- Keywords
- time series clustering, unsupervised clustering, big data, clustering algorithms, pattern discovery, gene expression data, stock market analysis, clustering efficiency
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