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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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Burugupalli, S., Smith, A., Oshlensky, G., Huynh, K., Giles, C., Wang, T., George, A. D., Paul, S., Nguyen, A., Duong, T., et al. (2022) · eLife · doi:10.7554/elife.72779 · Cited by 29
“We utilised dynamic time warping distance as a dissimilarity measurement (Aghabozorgi et al., 2015).”
MethodZhou, G.-W., Bai, M.-L., Zhao, X., Li, J., Li, Q., Liu, J.-F., & Yu, D.-R. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.112038 · Cited by 22
Dynamical Wasserstein Barycenters for Time-series ModelingPreprint
Cheng, K. C., Aeron, S., Hughes, M., & Miller, E. (2021) · Neural Information Processing Systems · doi:10.48550/arxiv.2110.06741 · Cited by 22
“Instead of modeling this data directly, to improve smoothness we model the empirical distribution of sliding windows of 2n+ 1 samples (Aghabozorgi et al., 2015) strided by δ samples.”
MethodEom, S., Jang, M., & Ji, N.-S. (2021) · The Professional Geographer · doi:10.1080/00330124.2021.1949729 · Cited by 25
A Spatio-Temporal Visualization Approach of PM10 Concentration Data in Metropolitan Lima
Encalada-Malca, A. A., Cochachi-Bustamante, J. D., Rodrigues, P., Salas, R., & López-Gonzales, J. (2021) · Atmosphere · doi:10.3390/atmos12050609 · Cited by 22
“In addition, clustering analysis is useful for detecting similar patterns in the data that comprises multiple time series [33,34].”
MethodLi, J., Hassan, D., Brewer, S. C., & Sitzenfrei, R. (2020) · Water · doi:10.3390/w12092433 · Cited by 22
Wearables and the Quantified Self: Systematic Benchmarking of Physiological Sensors
Sagl, G., Resch, B., Petutschnig, A., Kyriakou, K., Liedlgruber, M., & Wilhelm, F. H. (2019) · Sensors · doi:10.3390/s19204448 · Cited by 23
“A comprehensive review, however, is out of the scope of this paper—the interested reader may refer to [19,20,23,24], among other work.”
BackgroundDurán-Rosal, A. M., Gutiérrez, P. A., Carmona-Poyato, Á., & Hervás-Martínez, C. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.05.129 · Cited by 22
Demand cycles and market segmentation in bicycle sharing
Westland, J., Mou, J., & Yin, D. (2019) · Information Processing & Management · doi:10.1016/j.ipm.2018.09.006 · Cited by 22
A review on web content popularity prediction: Issues and open challenges
Moniz, N., & Torgo, L. (2019) · Online Social Networks and Media · doi:10.1016/j.osnem.2019.05.002 · Cited by 25
Mao, W.-T., He, J.-L., Sun, B., & Wang, L. (2021) · IEEE Access · doi:10.1109/access.2021.3117002 · Cited by 22
“For time series clustering, various similarity metrics have been proposed [26].”
BackgroundFuse, Y., Murphy, S. N., Ikari, H., Takahashi, A., Fuse, K., & Kawakami, E. (2025) · Allergology International · doi:10.1016/j.alit.2025.06.005 · Cited by 25
Data-driven classification of ligand unbinding pathways
Ray, D., & Parrinello, M. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2313542121 · Cited by 21
A Deep Dive into Perturbations as Evaluation Technique for Time Series XAI
Schlegel, U., & Keim, D. (2023) · xAI · doi:10.48550/arxiv.2307.05104 · Cited by 21
“We apply the Euclidean and cosine distances to the datasets as these are common distance functions for time series [2] to collect the changes of the perturbation function g.”
MethodA Machine Learning-based Characterization Framework for Parametric Representation of Liquid Sloshing
Luo, X., Kareem, A., Yu, L.-T., & Yoo, S. (2023) · Results in Engineering · doi:10.1016/j.rineng.2023.101148 · Cited by 21
Zhan, Q., Yang, C., & Liu, H.-M. (2022) · Geo-Spatial Information Science · doi:10.1080/10095020.2022.2085187 · Cited by 21
“…extension of Euclidean distance, Dynamic Time Warping (DTW) has been proposed as an extension of Euclidean distance to uncover temporal commonalities concealed behind phase variation using a dynamic planning technique (Aghabozorgi, Shirkhorshidi, and Wah 2015; Liu et al. 2018; Warren Liao 2005).”
MethodInfluentialRobust KPI Anomaly Detection for Large-Scale Software Services with Partial Labels
Zhang, S., Zhao, C., Sui, Y., Su, Y., Sun, Y., Zhang, Y., Pei, D., & Wang, Y. (2021) · Proceedings/Proceedings - International Symposium on Software Reliability Engineering · doi:10.1109/issre52982.2021.00023 · Cited by 25
“According to [38], widely used clustering algorithms can be roughly divided into several groups, including Partitioning, Hierarchical, Grid-based, Model-based, and Density-based algorithms.”
MethodHegg, J. C., & Kennedy, B. (2021) · bioRxiv · doi:10.1101/2021.04.19.440490 · Cited by 21
“Recent advances in time series analysis techniques may be an example of just such a technique (Aghabozorgi et al. 2015).”
BackgroundFactor Modeling for Clustering High-Dimensional Time Series
Zhang, B., Pan, G.-M., Yao, Q., & Zhou, W. (2021) · Journal of the American Statistical Association · doi:10.1080/01621459.2023.2183132 · Cited by 21
“…a large number of time series gains increasing momentum (Esling and Agon 2012), due to mining large and complex data recorded over time in business, finance, biology, medicine, climate, energy, environment, psychology, multimedia and other areas (Table 1 of Aghabozorgi, Shirkhorshid, and Wah 2015).”
Naglič, M., Popov, M., van der Meijden, M. V. D., & Terzija, V. (2020) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2019.2962246 · Cited by 21
“in [32], a wide variety of distance measures can be used, in particular, to assess the generators’ slow coherency indices.”
BackgroundRojas, F., Valenzuela, O., & Rojas, I. (2020) · medRxiv · doi:10.1101/2020.06.29.20142364 · Cited by 21
“The methods used in the area of time series clustering [11, 17] are usually based in conventional clustering algorithm by substituting standard distance measurements with a more suitable distance to compare time series (raw methods) or converting series into normal data and using directly classical algorithms (Feature…”
BackgroundMethodAnalyzing continuous infrasound from Stromboli volcano, Italy using unsupervised machine learning
Witsil, A. J. C., & Johnson, J. B. (2020) · Computational Geosciences · doi:10.1016/j.cageo.2020.104494 · Cited by 21
Fast Exact Dynamic Time Warping on Run-Length Encoded Time Series
Froese, V., Jain, B. J., Rymar, M., & Weller, M. (2022) · Algorithmica · doi:10.1007/s00453-022-01038-3 · Cited by 9
“Dynamic Time Warping (DTW) [18] is an established method which is used in numerous time series mining applications [1, 3, 4, 21].”
MethodYe, Y., Jiang, J., Ge, B.-F., Dou, Y., & Yang, K. (2018) · Knowledge and Information Systems · doi:10.1007/s10115-018-1264-0 · Cited by 21
Swain, S., Gupta, R., Ratnayake, K., Priyanka, P. D., Singh, R., Jana, S., Mitra, K., Karunarathne, A., & Giri, L. (2018) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.8b00297 · Cited by 21
Hierarchical clustering of unequal-length time series with area-based shape distance
Wang, X., Yu, F., Pedrycz, W., & Wang, J.-Y. (2018) · Soft Computing - A Fusion of Foundations, Methodologies and Applications · doi:10.1007/s00500-018-3287-6 · Cited by 21
“For more studies on equal-length time-series clustering, the readers can refer to some papers (Aghabozorgi et al. 2015; Liao 2005; Mori et al. 2015). However, due to various reasons, the data set we are faced with is not necessarily complete, and unequal-length time series are commonly encountered. An example is the o…”
BackgroundInfluentialPattern Clustering of Hysteresis Time Series With Multivalued Mapping Using Tensor Decomposition
He, H., & Tan, Y.-H. (2018) · IEEE Transactions on Systems, Man, and Cybernetics: Systems · doi:10.1109/tsmc.2017.2737578 · Cited by 21
“Currently, the SPCA has been expanded and combined with other pattern recognition approaches for various clustering applications of multivariate time series.”
BackgroundMethodInfluentialA shape-based adaptive segmentation of time-series using particle swarm optimization
Kamalzadeh, H., Ahmadi, A., & Mansour, S. (2017) · Information Systems · doi:10.1016/j.is.2017.03.004 · Cited by 21
“This taxonomy of representation methods is completed in [1] by putting the representation methods into four categories: data adaptive, non-data adaptive, model-based and data dictated.”
BackgroundMethodInfluentialMiraftabzadeh, S., Longo, M., & Brenna, M. (2023) · IEEE Access · doi:10.1109/access.2023.3292516 · Cited by 21
“The data transformation or data reduction and projection in the KDD process helps prepare the data for analysis by converting it into a form that the machine learning algorithm can interpret more efficiently [9], [46], [47].”
MethodAbbasimehr, H., & Baghery, F. (2022) · Expert systems with applications · doi:10.1016/j.eswa.2022.117584 · Cited by 20
Mithra: Anomaly Detection as an Oracle for Cyberphysical Systems
Afzal, A., Goues, C. L., & Timperley, C. (2021) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2021.3120680 · Cited by 20
“are based on comparing differences in shape are often superior in terms of performance than those that compare differences in time [9], [93].”
BackgroundClassifying Major Explosions and Paroxysms at Stromboli Volcano (Italy) from Space
Corradino, C., Amato, E., Torrisi, F., Calvari, S., & Negro, C. D. (2021) · Remote Sensing · doi:10.3390/rs13204080 · Cited by 20
A Method for Structure Breaking Point Detection in Engine Oil Pressure Data
Grzesiek, A., Zimroz, R., Śliwiński, P., Gomolla, N., & Wyłomańska, A. (2021) · Energies · doi:10.3390/en14175496 · Cited by 20
Ohana-Levi, N., Knipper, K., Kustas, W., Anderson, M. C., Netzer, Y., Gao, F., Alsina, M. D. M., Sanchez, L., & Karnieli, A. (2020) · Remote Sensing · doi:10.3390/rs12152436 · Cited by 20
“This whole-sequence matching considers the entire lengths of the time series during the distance calculation [82].”
BackgroundInfluentialWilli, J., Süss, H., & Ehlert, U. (2020) · Women s Midlife Health · doi:10.1186/s40695-020-00052-1 · Cited by 19
Using Unsupervised Machine Learning to Characterize Capillary Flow and Residual Trapping
Ni, H., & Benson, S. (2020) · Water Resources Research · doi:10.1029/2020wr027473 · Cited by 20
“For time series of equal lengths, arithmetic averaging is often used for each dimension of the data points in the same cluster (Aghabozorgi et al., 2015).”
BackgroundMethodInfluentialOptimal Transport Based Change Point Detection and Time Series Segment Clustering
Cheng, K. C., Aeron, S., Hughes, M. C., Hussey, E., & Miller, E. (2019) · IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp40776.2020.9053344 · Cited by 20
“Applications here overlap with those of CPD [4].”
ResultKeshavarzi, A., Toroghihaghighat, A., & Bohlouli, M. (2019) · Computing · doi:10.1007/s00607-019-00747-y · Cited by 20
Di, Z., Chang, M., Guo, P.-K., Li, Y., & Chang, Y. (2019) · Water · doi:10.3390/w11061268 · Cited by 20
“Clustering algorithms, as established unsupervised machine learning models, have been used to analyze data from a wide range of disciplines, such as gene expression data in biology and stock market financial data [18,19], yet have been rarely applied to the water environment due to the lack of data [11,20–23].”
MethodOn-line Elastic Similarity Measures for time series
Oregi, I., Pérez, A., del Ser, J., & Lozano, J. A. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2018.12.007 · Cited by 20
A Survey on Big Data Pre-processing
Guan, Z., Ji, T., Xu, Q., Ma, Y., & Hong, X. (2017) · International Conference on Applied Computing and Information Technology / International Conference on Computational Science and Intelligence (ACIT-CSI) · doi:10.1109/acit-csii-bcd.2017.49 · Cited by 23
“Besides, the overall amount of data can be reduced by performing temporal compression [60].”
BackgroundGauthier, R., Largouët, C., Rozé, L., & Dourmad, J.-Y. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106329 · Cited by 23
“Clustering can be applied to time-series and is defined as follows (Aghabozorgi et al., 2015): Homogeneous time-series are grouped together based on a certain similarity measure that maximizes inter-cluster distance and minimizes intra-cluster variance.”
BackgroundMethodInfluentialYerbury, L. W., Campello, R., Livingston, G., Goldsworthy, M., & O’Neil, L. (2024) · Applied Energy · doi:10.1016/j.apenergy.2025.125811 · Cited by 19
“The result is an enormous catalogue of components and approaches advanced in the literature — including a host of time series specific methods [4].”
InfluentialGlobal sourcing under tariffs: A perspective of time series analysis
Zhang, D. J., Dabadghao, S., & Udenio, M. (2024) · International Journal of Production Economics · doi:10.1016/j.ijpe.2024.109473 · Cited by 19
Constructing hierarchical time series through clustering: Is there an optimal way for forecasting?
Zhang, B., Panagiotelis, A., & Li, H. (2024) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2024.10.002 · Cited by 19
“Inspired by the comprehensive overview of time series clustering by Aghabozorgi et al. (2015), we consider various approaches based on three key components, namely time series representations , distance measures , and clustering algorithms .”
MethodRadovanović, A., & Milanović, J. V. (2023) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2023.3281498 · Cited by 22
“AgglomerativeHC algorithmorganizesclusteringobjectsintoahierarchicalstruc-ture[22],[23].”
MethodBecchi, M., Fantolino, F., & Pavan, G. M. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2403771121 · Cited by 17
“…most common clustering algorithms are either built to handle static datasets, or to perform whole time-series clustering [44, 45, 46, 47], and are thus not well-suited to obtain a single-point (microscopic-level) clustering of the local dynamical events occurring in the time-series [48, 49, 50].”
BackgroundSaltwater intrusion early warning in Pearl river Delta based on the temporal clustering method.
Weng, P., Tian, Y., Zhou, H., Zheng, Y., & Jiang, Y. (2023) · Journal of Environmental Management · doi:10.1016/j.jenvman.2023.119443 · Cited by 19
Odyssey: An Engine Enabling The Time-Series Clustering Journey
Paparrizos, J., & Reddy, S. P. T. (2023) · Proceedings of the VLDB Endowment · doi:10.14778/3611540.3611622 · Cited by 19
“Among analytical tasks for time series [6, 7, 11, 12, 24, 25, 31], clustering is one of the most widely used as it does not require annotated data or human supervision [4, 5, 20].”
BackgroundMetaheuristic-based time series clustering for anomaly detection in manufacturing industry
Suh, W. H., Oh, S., & Ahn, C. W. (2023) · Applied Intelligence · doi:10.1007/s10489-023-04594-5 · Cited by 21
“Metaheuristic optimization to search for optimal clusters and probabilistic models by estimation of distribution algorithm (EDA) [2,5] [3,6,9] [1] [7] [8,10] e.g. clustering techniques.”
BackgroundMethodInfluentialTime series clustering of COVID-19 pandemic-related data
Luo, Z., Zhang, L., Liu, N., & Wu, Y. (2023) · Data Science and Management · doi:10.1016/j.dsm.2023.03.003 · Cited by 21
“Some studies have reduced the dimensionality of time series and extracted features, i.e., using a feature-based approach (Aghabozorgi et al., 2015) and then clustering, which effectively addresses the high dimensionality characteristics of time series.”
Trần, T., Bruce, D., Huang, C.-Y., Tran, D., Myint, S., & Nguyen, D. (2023) · GIScience & Remote Sensing · doi:10.1080/15481603.2022.2163070 · Cited by 19
“A time-series clustering analysis consists of two parts: measuring similarity and performing clustering algo-rithm (Aghabozorgi, Shirkhorshidi, and Wah 2015).”
MethodYe, Y., Wang, Y., Liao, J., Chen, J., Zou, Y.-F., Liu, Y., & Feng, C. (2022) · Remote Sensing · doi:10.3390/rs14153518 · Cited by 19
Robust System Instance Clustering for Large-Scale Web Services
Zhang, S., Li, D., Zhong, Z., Zhu, J., Liang, M., Luo, J., Sun, Y., Su, Y., Xia, S., Hu, Z., et al. (2022) · International World Wide Web Conference (WWW) · doi:10.1145/3485447.3511983 · Cited by 17
“In this experiment, we replace our distance measurement method D MTS with other popular ones: Manhattan distance [35], shape-based distance (SBD) [38], mean squared error (MSE) [4], normalized Euclidean distance (NED) [9], Wasserstein distance [42], Jensen-Shannon divergence (JSD) [21] and Pearson correlation coeffici…”
MethodInfluentialDeep Learning Techniques for Smart Meter Data Analytics: A Review
Eskandarnia, E., Al-Ammal, H., Ksantini, R., & Hammad, M. (2022) · SN Computer Science · doi:10.1007/s42979-022-01161-6 · Cited by 21
“Clustering validity is usually evaluated with visualization, the goodness of fit as well as scalar measurements [61].”
BackgroundLuca, G. D., & Zuccolotto, P. (2021) · International Journal of Approximate Reasoning · doi:10.1016/j.ijar.2021.09.004 · Cited by 19
CBERS DATA CUBE: A POWERFUL TECHNOLOGY FOR MAPPING AND MONITORING BRAZILIAN BIOMES
Picoli, M., Simões, R., Chaves, M., Santos, L., Sánchez, A., Soares, A., Sanches, I., Ferreira, K., & de Queiroz, G. R. (2020) · doi:10.5194/isprs-annals-v-3-2020-533-2020 · Cited by 19
“SOM is a suitable clustering method when working with timeseries data (Aghabozorgi et al., 2015), it is an unsupervised neural network where the input layer is the sample dataset, and the output layer is a data set of grouped neurons.”
MethodZhang, Y.-H., & Zhao, H. (2020) · Canadian journal of remote sensing · doi:10.1080/07038992.2020.1740083 · Cited by 19
“The change magnitude image is generally calculated by the distance measured between the time series data (Aghabozorgi et al. 2015).”
MethodNovel evolutionary multi-objective soft subspace clustering algorithm for credit risk assessment
Liu, C., Xie, J., Zhao, Q., Xie, Q., & Liu, C.-Q. (2019) · Expert systems with applications · doi:10.1016/j.eswa.2019.112827 · Cited by 19
Measuring Traffic in Cities Through a Large-Scale Online Platform
Verendel, V., & Yeh, S. (2019) · Journal of Big Data Analytics in Transportation · doi:10.1007/s42421-019-00007-7 · Cited by 19
Vehicle response-based track geometry assessment using multi-body simulation
Kraft, S., Causse, J., & Coudert, F. (2018) · doi:10.1080/00423114.2017.1359418 · Cited by 19
Statistical analysis of factors influencing the results of enterprises in dairy industry
Náglová, Z., Boberova, B., Horáková, T., & Smutka, L. (2017) · doi:10.17221/353/2015-agricecon · Cited by 19
“Cluster analysis is used to figure out how some categories behave and that some do (not) show a similar behaviour, it corresponds to the appropriate inclusion into clusters (Aghabozorgi et al. 2015). The hierarchical cluster analysis was used to create clusters (Gomez et al. 2015), which results in the creation of a h…”
BackgroundMethodInfluentialAslan, S., Yozgatlıgil, C., & İyigün, C. (2017) · Annals of Operations Research · doi:10.1007/s10479-017-2659-0 · Cited by 21
“Comprehensive surveys based on different aspects of time series clustering can be seen in Liao (2005), Fu (2011) andAghabozorgi et al. (2015). Clustering of multiple time series that is only based on pattern (i.”
BackgroundYang, W. F. Z., Chowdhury, A., Sparby, T., & Sacchet, M. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120968 · Cited by 18
“As we have done previously (Yang et al., 2024), hierarchical time series clustering using dynamic time warping was used to extract the optimal number of polynomial trends based on their shape before running the linear mixed model analysis to assess changes in brain activity across SoIs, (Aghabozorgi et al., 2015; Sako…”
Data-driven statistical optimization of a groundwater monitoring network
Meggiorin, M., Naranjo-Fernández, N., Passadore, G., Sottani, A., Botter, G., & Rinaldo, A. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.130667 · Cited by 18
A user‐friendly guide to using distance measures to compare time series in ecology
Dove, S., Böhm, M., Freeman, R., Jellesmark, S., & Murrell, D. J. (2023) · Ecology and Evolution · doi:10.1002/ece3.10520 · Cited by 20
“For example, clustering involves grouping similar time series together by maximizing the similarity within groups and minimizing the similarity between groups (Aghabozorgi et al., 2015 ; Esling & Agon, 2012; Liao, 2005 ).”
Clustering time series under trend-oriented fuzzy information granulation
Yang, X.-Y., Yu, F., Pedrycz, W., & Li, Z.-W. (2023) · Applied Soft Computing · doi:10.1016/j.asoc.2023.110284 · Cited by 18
Time series clustering via matrix profile and community detection
Li, H., Wu, X., Wan, X.-J., & Lin, W. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101771 · Cited by 18
A key review on security and privacy of big data: issues, challenges, and future research directions
Demirol, D., Das, R., & Hanbay, D. (2022) · Signal, Image and Video Processing · doi:10.1007/s11760-022-02341-w · Cited by 18
“Large amounts of data or flow-ing data cannot be preprocessed within a tolerable time and quality at an acceptable threshold value by conventional data reduction algorithms [35].”
BackgroundRegional Economic Disparities in Europe: Time-Series Clustering of NUTS 3 Regions
López-Villuendas, A. M., & del Campo, C. (2022) · International regional science review · doi:10.1177/01600176221125703 · Cited by 18
“In the literature, three ways of addressing clustering time-series are presented (Aghabozorgi, Seyed Shirkhorshidi, and Ying Wah 2015): feature-based, shape-based and model-based approaches.”
BackgroundUDAVA: An Unsupervised Learning Pipeline for Sensor Data Validation in Manufacturing
Husom, E. J., Tverdal, S., Goknil, A., & Sen, S. (2022) · 2022 IEEE/ACM 1st International Conference on AI Engineering – Software Engineering for AI (CAIN) · doi:10.1145/3522664.3528603 · Cited by 18
Choi, Y., An, N., Hong, S., Cho, H., Lim, J., Han, I., Moon, I., & Kim, J. (2022) · Computers & Chemical Engineering · doi:10.1016/j.compchemeng.2022.107758 · Cited by 20
A Review on Clustering Techniques: Creating Better User Experience for Online Roadshow
Lim, Z.-Y., Ong, L.-Y., & Leow, M.-C. (2021) · Future Internet · doi:10.3390/fi13090233 · Cited by 22
Bañales, S., Dormido, R., & Duro, N. (2021) · Energies · doi:10.3390/en14123458 · Cited by 17
“The challenges associated with time series clustering are well recognized, and they include high dimensionality and the definition of similarity taking the time dimen‐ sion into account, from which three key research areas are derived: dimensionality reduc‐ tion; clustering approach, which includes the choice of dista…”
BackgroundK-MDTSC: K-Multi-Dimensional Time-Series Clustering Algorithm
Giordano, D., Mellia, M., & Cerquitelli, T. (2021) · Electronics · doi:10.3390/electronics10101166 · Cited by 18
“To update it, in 2015, authors in [5] proposed a new overview of ten years of time-series clustering applied in different fields.”
BackgroundMethodKim, D., Yun, J., Kim, K., & Lee, S. (2021) · Sustainability · doi:10.3390/su13063302 · Cited by 18
“Because the goal of the classification is to discover a new set of clusters, the new groups are meaningful in themselves [78,79].”
BackgroundUnsupervised machine learning techniques to prevent faults in railroad switch machines
Soares, N., de Aguiar, E. P., Souza, A. C., & Goliatt, L. (2021) · International Journal of Critical Infrastructure Protection · doi:10.1016/j.ijcip.2021.100423 · Cited by 23
Keshavarzi, A., Haghighat, A., & Bohlouli, M. (2021) · Expert systems with applications · doi:10.1016/j.eswa.2020.113840 · Cited by 18
Linking the performance of a data-limited empirical catch rule to life-history traits
Fischer, S. H., Oliveira, J. A. A. D., & Kell, L. T. (2020) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsaa054 · Cited by 26
Toussaint, W., & Moodley, D. (2020) · S. Afr. Comput. J. · doi:10.18489/sacj.v32i2.845 · Cited by 18
“4 Specifying Application Requirements for External Validation It is commonly known that the performance of clustering algorithms depends on both the data characteristics and the clustering objective [26].”
BackgroundNandi, A. (2020) · IEEE Access · doi:10.1109/access.2020.3000860 · Cited by 18
A new approach for optimal time-series segmentation
Carmona-Poyato, A., Fernández-García, N. L., Madrid-Cuevas, F. J., & Durán-Rosal, A. M. (2020) · Pattern Recognition Letters · doi:10.1016/j.patrec.2020.04.006 · Cited by 19
Analysis of air pollution time series using complexity-invariant distance and information measures
Amato, F., Laib, M., Guignard, F., & Kanevski, M. (2019) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2020.124391 · Cited by 18
“There are many measures and features proposed in time series clustering, see, for example a review in [46] .”
BackgroundStashevsky, P., Яковина, И. Н., Falconi, T. M. A., & Naumova, E. N. (2019) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph16122083 · Cited by 22
“Hierarchical clustering is gaining popularity in many fields of life sciences as an effective technique for identifying distinct homogeneous clusters [14,15].”
MethodAn Open-Boundary Locally Weighted Dynamic Time Warping Method for Cropland Mapping
Guan, X., Liu, G.-H., Huang, C., Meng, X.-L., Liu, Q.-S., Wu, C.-S., Ablat, X., Chen, Z., & Wang, Q. (2018) · ISPRS Int. J. Geo Inf. · doi:10.3390/ijgi7020075 · Cited by 18
“DTW is very good in dealing with temporal drift [26].”
BackgroundŁuczak, M. (2018) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-171393 · Cited by 18
On the use of evolutionary time series analysis for segmenting paleoclimate data
Pérez-Ortiz, M., Durán-Rosal, A. M., Gutiérrez, P. A., Sánchez-Monedero, J., Nikolaou, A., Fernández-Navarro, F., & Hervás-Martínez, C. (2017) · Neurocomputing · doi:10.1016/j.neucom.2016.11.101 · Cited by 18
“The GA segments the data trying to obtain diverse clusters of segments based on six statistical properties (for more information about time series clustering see [20]).”
BackgroundSimilarity Measure Based on Incremental Warping Window for Time Series Data Mining
Li, H., & Wang, C. (2018) · IEEE Access · doi:10.1109/access.2018.2889792 · Cited by 23
“Time series data mining is also one of the most challenged directions [1], [2] and attracts much attention [3]–[5].”
BackgroundRamirez, E., Wimmer, M., & Atzmueller, M. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-37446-4_11 · Cited by 17
Deep time-series clustering via latent representation alignment
Lee, S.-H., Choi, C., & Son, Y. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.112434 · Cited by 17
Yılmaz, H., & Yağmahan, B. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112336 · Cited by 20
Li, H., Dai, Y., Liu, X.-C., Tao, Z., Zhang, J., & Liu, X.-H. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2023.117244 · Cited by 17
Time-series clustering for sensor fault detection in large-scale Cyber-Physical Systems
Alwan, A. A., Brimicombe, A., Ciupala, M., Ghorashi, S., Baravalle, A., & Falcarin, P. (2022) · Comput. Networks · doi:10.1016/j.comnet.2022.109384 · Cited by 17
López-Oriona, Á., D'Urso, P., Vilar, J. A., & Lafuente-Rego, B. (2022) · IEEE transactions on fuzzy systems · doi:10.1109/tfuzz.2021.3136005 · Cited by 17
“Extensive reviews on the topic can be seen in [1]–[4], and more recently in the monograph by [5].”
BackgroundHydrological Time Series Clustering: A Case Study of Telemetry Stations in Thailand
Prakaisak, I., & Wongchaisuwat, P. (2022) · Water · doi:10.3390/w14132095 · Cited by 17
Travel pattern-based bus trip origin-destination estimation using smart card data
Lee, I., Cho, S.-H., Kim, K., Kho, S., & Kim, D.-K. (2022) · PLoS ONE · doi:10.1371/journal.pone.0270346 · Cited by 17
“Model-based clustering is employed to recover the original model from a dataset [27], which is suitable for modeling and analyzing data characteristics, such as travel patterns.”
MethodHow did COVID-19 change what people buy: Evidence from a supermarket chain
Zuokas, D., Gul, E., & Lim, A. (2022) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2022.103010 · Cited by 17
“Hierarchical clustering for time series is a well-researched and widely applied technique in data analytics (Aghabozorgi et al., 2015).”
A modified Kohonen map algorithm for clustering time series data
Krishnan, K. J., & Mitra, K. (2022) · Expert systems with applications · doi:10.1016/j.eswa.2022.117249 · Cited by 17
Pang, Y.-L., Shi, M.-L., Zhang, L.-Y., Sun, W., & Song, X.-G. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2022.108362 · Cited by 17
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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