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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
Research works printed but not linked: 225
- Rai, 2010, A survey of clustering techniques, Int. J. Comput. Appl., 7, 1
- V. Niennattrakul, C. Ratanamahatana, On clustering multimedia time series data using k-means and dynamic time warping, in: Proceedings of the International Conference on Multimedia and Ubiquitous Engineering, 2007, MUE ’07, 2007, pp. 733–738. · DOI
- C. Ratanamahatana, Multimedia retrieval using time series representation and relevance feedback, in: Proceedings of 8th International Conference on Asian Digital Libraries (ICADL2005), 2005, pp. 400–405. · DOI
- C. Ratanamahatana, V. Niennattrakul, Clustering multimedia data using time series, in: Proceedings of the International Conference on Hybrid Information Technology, 2006, ICHIT ’06, 2006, pp. 372–379. · DOI
- J. Lin, E. Keogh, S. Lonardi, J. Lankford, D. Nystrom, Visually mining and monitoring massive time series, in: Proceedings of 2004 ACM SIGKDD International Conference on Knowledge Discovery and data Mining – KDD ’04, 2004, p. 460. · DOI
- Keogh, 2003, On the need for time series data mining benchmarks: a survey and empirical demonstration, Data Min. Knowl. Discov., 7, 349, 10.1023/A:1024988512476 · DOI
- K. Haigh, W. Foslien, and V. Guralnik, Visual query language: finding patterns in and relationships among time series data, Seventh Workshop on Mining Scientific And Engineering Datasets, 2004, pp. 324–332.
- Keogh, 2004, Segmenting time series: a survey and novel approach, Data Min. Time Ser. Databases, 57, 1, 10.1142/9789812565402_0001 · DOI
- J. Lin, E. Keogh, S. Lonardi, and B. Chiu, A symbolic representation of time series, with implications for streaming algorithms, in: Proceedings of 8th ACM SIGMOD Workshop on Research Issues Data Mining and Knowledge Discovery – DMKD ’03, 2003, p. 2. · DOI
- J. Zakaria, S. Rotschafer, A. Mueen, K. Razak, E. Keogh, Mining massive archives of mice sounds with symbolized representations, in: SIGKDD, 2012, pp. 1–10. · DOI
- T. Rakthanmanon, A.B. Campana, G. Batista, J. Zakaria, E. Keogh, Searching and mining trillions of time series subsequences under dynamic time warping, in: proceedings of the Conference on Knowledge Discovery and Data Mining, 2012, pp. 262–270. · DOI
- E. Keogh, A decade of progress in indexing and mining large time series databases, in: Proceedings of the International Conference on Very Large Data Bases (VLDB), 2006, pp. 1268–1268.
- Laxman, 2006, A survey of temporal data mining, Sadhana, 31, 173, 10.1007/BF02719780 · DOI
- Kavitha, 2010, Clustering time series data stream—a literature survey, Int. J. Comput. Sci. Inf. Secur., 8
- C. Antunes, A.L. Oliveira, Temporal data mining: an overview, in: KDD Workshop on Temporal Data Mining, 2001, pp. 1–13.
- Warrenliao, 2005, Clustering of time series data—a survey, Pattern Recognit., 38, 1857, 10.1016/j.patcog.2005.01.025 · DOI
- Rani, 2012, Recent techniques of clustering of time series data: a survey, Int. J. Comput. Appl, 52, 1
- Lin, 2004, Iterative incremental clustering of time series, Adv. Database Technol, 2004, 521
- R. Kumar, P. Nagabhushan, Time series as a point—a novel approach for time series cluster visualization in: Proceedings of the Conference on Data Mining, 2006, pp. 24–29.
- Faloutsos, 1994, Fast subsequence matching in time-series databases, ACM SIGMOD Rec., 23, 419, 10.1145/191843.191925 · DOI
- Wang, 2006, Characteristic-based clustering for time series data, Data Min. Knowl. Discov., 13, 335, 10.1007/s10618-005-0039-x · DOI
- Chiş, 2009, Clustering time series data: an evolutionary approach, Found. Comput. Intell., 6, 193
- J. Lin, E. Keogh, W. Truppel, Clustering of streaming time series is meaningless, in: Proceedings of 8th ACM SIGMOD Workshop on Research Issues Data Mining and Knowlegde Discovery DMKD 03, 2003, p. 56. · DOI
- Keogh, 2000, A simple dimensionality reduction technique for fast similarity search in large time series databases, Knowl. Inf. Syst., 1805, 122
- Zhang, 2006, Unsupervised feature extraction for time series clustering using orthogonal wavelet transform, Informatica, 30, 305
- H. Wang, W. Wang, J. Yang, P.P.S. Yu, Clustering by pattern similarity in large data sets, in: Proceedings of 2002 ACM SIGMOD International Conference Management data – SIGMOD ’02, vol. 2, 2002, p. 394. · DOI
- Das, 1998, Rule discovery from time series,, Knowl. Discov. Data Min, 98, 16
- T.C. Fu, F.L. Chung, V. Ng, R. Luk, Pattern discovery from stock time series using self-organizing maps, in: Workshop Notes of KDD2001 Workshop on Temporal Data Mining, 2001, pp. 26–29.
- B. Chiu, E. Keogh, S. Lonardi, Probabilistic discovery of time series motifs, in: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2003, pp. 493–498. · DOI
- E. Keogh, S. Lonardi, B.Y. Chiu, Finding surprising patterns in a time series database in linear time and space, in: Proceedings of the Eighth ACM SIGKDD, 2002, pp. 550–556. · DOI
- P.K. Chan, M.V. Mahoney, Modeling multiple time series for anomaly detection, in: Proceedings of Fifth IEEE International Conference on Data Mining, 2005, pp. 90–97. · DOI
- L. Wei, N. Kumar, V. Lolla, E. Keogh, Assumption-free anomaly detection in time series, in: Proceedings of the 17th International Conference on Scientific and Statistical Database Management, 2005, pp. 237–240.
- M. Leng, X. Lai, G. Tan, X. Xu, Time series representation for anomaly detection, in: Proceedings of 2nd IEEE International Conference on Computer Science and Information Technology, 2009, ICCSIT 2009, 2009, pp. 628–632. · DOI
- He, 2011, A new method for abrupt dynamic change detection of correlated time series,, Int. J. Climatol., 32, 1604, 10.1002/joc.2367 · DOI
- Sfetsos, 2004, Time series forecasting with a hybrid clustering scheme and pattern recognition, IEEE Trans. Syst. Man Cybern, 34, 399, 10.1109/TSMCA.2003.822270 · DOI
- Pavlidis, 2006, Financial forecasting through unsupervised clustering and neural networks, Oper. Res., 6, 103
- F. Ito, T. Hiroyasu, M. Miki, H. Yokouchi, Detection of Preference Shift Timing using Time-Series Clustering, 2009, pp. 1585–1590. · DOI
- D. Graves, W. Pedrycz Proximity fuzzy clustering and its application to time series clustering and prediction in: Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications ISDA10, 2010, pp. 49–54. · DOI
- Rebbapragada, 2009, Finding anomalous periodic time series, Mach. Learn., 74, 281, 10.1007/s10994-008-5093-3 · DOI
- Subhani, 2010, Multiple gene expression profile alignment for microarray time-series data clustering, Bioinformatics, 26, 2281, 10.1093/bioinformatics/btq422 · DOI
- Fujita, 2012, Functional clustering of time series gene expression data by Granger causality, BMC Syst. Biol., 6, 137, 10.1186/1752-0509-6-137 · DOI
- Möller-Levet, 2003, Fuzzy clustering of short time-series and unevenly distributed sampling points, Adv. Intell. Data Anal., 330
- Ernst, 2005, Clustering short time series gene expression data, Bioinforma., 21, i159, 10.1093/bioinformatics/bti1022 · DOI
- Pyatnitskiy, 2014, Clustering gene expression regulators: new approach to disease subtyping, PLoS One, 9, e84955, 10.1371/journal.pone.0084955 · DOI
- M. Steinbach, P.N. Tan, V. Kumar, S. Klooster, and C. Potter, Discovery of climate indices using clustering, in: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery And data Mining, 2003, pp. 446–455. · DOI
- Ji, 2013, A dynamic fuzzy cluster algorithm for time series, Abstr. Appl. Anal., 2013, 1
- Elangasinghe, 2014, Complex time series analysis of PM10 and PM2.5 for a coastal site using artificial neural network modelling and k-means clustering, Atmos. Environ., 94, 106, 10.1016/j.atmosenv.2014.04.051 · DOI
- Košmelj, 1990, Cross-sectional approach for clustering time varying data, J. Classif, 7, 10.1007/BF01889706 · DOI
- Iglesias, 2013, Analysis of similarity measures in times series clustering for the discovery of building energy patterns, Energies, 6, 579, 10.3390/en6020579 · DOI
- Scotto, 2010, Clustering time series of sea levels: extreme value approach, J. Waterw. Port, Coastal, Ocean Eng., 136, 215, 10.1061/(ASCE)WW.1943-5460.0000045 · DOI
- Shumway, 2003, Time-frequency clustering and discriminant analysis, Stat. Probab. Lett, 63, 307, 10.1016/S0167-7152(03)00095-6 · DOI
- Liu, 2014, Polarization of forecast densities: a new approach to time series classification, Comput. Stat. Data Anal., 70, 345, 10.1016/j.csda.2013.10.008 · DOI
- Sadahiro, 2014, Exploratory analysis of time series data: detection of partial similarities, clustering, and visualization, Comput. Environ. Urban Syst., 45, 24, 10.1016/j.compenvurbsys.2014.02.001 · DOI
- Gorji Sefidmazgi, 2014, Trend analysis using non-stationary time series clustering based on the finite element method, Nonlinear Process. Geophys., 21, 605, 10.5194/npg-21-605-2014 · DOI
- M. Kumar, N.R. Patel, Clustering seasonality patterns in the presence of errors, in: Proceedings of Eighth ACM SIGKDD, 2002, pp. 557–563. · DOI
- A.J. Bagnall, G. Janacek, B. De la Iglesia, M. Zhang, Clustering time series from mixture polynomial models with discretised data in: Proceedings of the Second Australasian Data Mining Workshop, 2003, pp. 105–120. · DOI
- H. Guan, Q. Jiang, Cluster financial time series for portfolio, in: Proceedings of the International Conference on Wavelet Analysis and Pattern Recognition, 2007, pp. 851–856. · DOI
- C. Guo, H. Jia, N. Zhang, Time series clustering based on ICA for stock data analysis, in: Proceedings of 4th International Conference on Wireless Communications, Networking and Mobile Computing, 2008. WiCOM ’08, 2008, pp. 1–4. · DOI
- A. Stetco, X. Zeng, J. Keane, Fuzzy cluster analysis of financial time series and their volatility assessment, in: Proceedings of 2013 IEEE International Conference on Systems, Man, and Cybernetics, 2013, pp. 91–96. · DOI
- Aghabozorgi, 2014, Stock market co-movement assessment using a three-phase clustering method, Expert Syst. Appl., 41, 1301, 10.1016/j.eswa.2013.08.028 · DOI
- Hsu, 2014, A clustering time series model for the optimal hedge ratio decision making, Neurocomputing, 138, 358, 10.1016/j.neucom.2014.01.026 · DOI
- Wismüller, 2002, Cluster analysis of biomedical image time-series, Int. J. Comput. Vis, 46, 103, 10.1023/A:1013550313321 · DOI
- van den Heuvel, 2008, Normalized cut group clustering of resting-state fMRI data, PLoS One, 3, e2001, 10.1371/journal.pone.0002001 · DOI
- Gullo, 2011, A time series approach for clustering mass spectrometry data, J. Comput. Sci., 3, 344, 10.1016/j.jocs.2011.06.008 · DOI
- V. Kurbalija, J. Nachtwei, C. Von Bernstorff, C. von Bernstorff, H.-D. Burkhard, M. Ivanović, L. Fodor, Time-series mining in a psychological domain, in: Proceedings of the Fifth Balkan Conference in Informatics, 2012, pp. 58–63. · DOI
- M. Ramoni, P. Sebastiani, P. Cohen, Multivariate clustering by dynamics, in :Proceedings of the national Conference on Artificial Intelligence, 2000, pp. 633–638.
- Gopalapillai, 2014, Experimentation and analysis of time series data for rescue robotics, Recent Adv Intell Inf, 253, 443, 10.1007/978-3-319-01778-5_46 · DOI
- Tran, 2002, Fuzzy c-means clustering-based speaker verification, Adv. Soft Comput. 2002, 2275, 363
- Fong, 2012, Using hierarchical time series clustering algorithm and wavelet classifier for biometric voice classification, J. Biomed. Biotechnol., 215019
- Zhu, 2014, Social network users clustering based on multivariate time series of emotional behavior, J. China Univ. Posts Telecommun, 21, 21, 10.1016/S1005-8885(14)60282-X · DOI
- Keogh, 2005, Clustering of time-series subsequences is meaningless: implications for previous and future research, Knowl. Inf. Syst., 8, 154, 10.1007/s10115-004-0172-7 · DOI
- A. Gionis, H. Mannila, Finding recurrent sources in sequences, in: Proceedings of the Seventh Annual International Conference on RESEARCH in Computational Molecular Biology, 2003, pp. 123–130. · DOI
- Morchen, 2005, Extracting interpretable muscle activation patterns with time series knowledge mining, J. Knowl. BASED, 9, 197
- V. Hautamaki, P. Nykanen, P. Franti, Time-series clustering by approximate prototypes, in: Proceedings of 19th International Conference on Pattern Recognition, 2008, ICPR 2008, 2008, no. D, pp. 1–4. · DOI
- Mitsa, 2009, vol. 33
- E. Keogh, Hot sax: efficiently finding the most unusual time series subsequence, in: Proceedings of Fifth IEEE International Conference on Data Mining ICDM05, 2005, pp. 226–233. · DOI
- Ghysels, 2006, Predicting volatility: getting the most out of return data sampled at different frequencies, J. Econom, 131, 59, 10.1016/j.jeconom.2005.01.004 · DOI
- C. Ratanamahatana, E. Keogh, A.J. Bagnall, S. Lonardi, A novel bit level time series representation with implications for similarity search and clustering, in: Proceedings of 9th Pacific-Asian International Conference on Knowledge Discovery and Data Mining (PAKDD’05), 2005, pp. 771–777. · DOI
- Lin, 2007, Experiencing SAX: a novel symbolic representation of time series”, Data Min. Knowl. Discov., 15, 107, 10.1007/s10618-007-0064-z · DOI · Paper page
- K. Chan, A.W. Fu, Efficient time series matching by wavelets, in: Proceedings of 1999 15th International Conference on Data Engineering, vol. 15, no. 3, 1999, pp. 126–133. · DOI
- E. Keogh, M. Pazzani, An enhanced representation of time series which allows fast and accurate classification, clustering and relevance feedback, in: Proceedings of the 4th International Conference of Knowledge Discovery and Data Mining, 1998, pp. 239–241.
- Keogh, 2001, Locally adaptive dimensionality reduction for indexing large time series databases,, ACM SIGMOD Rec, 27, 151, 10.1145/376284.375680 · DOI
- I. Popivanov, R.J. Miller, Similarity search over time-series data using wavelets, in: ICDE ’02: Proceedings of the 18th International Conference on Data Engineering, 2002, pp. 212–224. · DOI
- Y.L. Wu, D. Agrawal, A. El Abbadi, “ comparison of DFT and DWT based similarity search in time-series databases, in: Proceedings of the Ninth International Conference on Information and Knowledge Management, 2000, pp. 488–495. · DOI
- B.K. Yi, C. Faloutsos, Fast time sequence indexing for arbitrary Lp norms, in: Proceedings of the 26th International Conference on Very Large Data Bases, 2000, pp. 385–394.
- Ding, 2008, Querying and mining of time series data: experimental comparison of representations and distance measures,, Proc. VLDB Endow, 1, 1542, 10.14778/1454159.1454226 · DOI
- Bagnall, 2006, A bit level representation for time series data mining with shape based similarity, Data Min. Knowl. Discov., 13, 11, 10.1007/s10618-005-0028-0 · DOI
- Shieh, 2009, iSAX: disk-aware mining and indexing of massive time series datasets, Data Min. Knowl. Discov., 19, 24, 10.1007/s10618-009-0125-6 · DOI
- Wang, 2012, Experimental comparison of representation methods and distance measures for time series data, Data Min. Knowl. Discov.
- Y. Morinaka, M. Yoshikawa, T. Amagasa, S. Uemura, The L-index: an indexing structure for efficient subsequence matching in time sequence databases, in: Proceedings of 5th PacificAisa Conference on Knowledge Discovery and Data Mining, 2001, pp. 51–60.
- H. Shatkay S.B. Zdonik, Approximate queries and representations for large data sequences, in: Proceedings of the Twelfth International Conference on Data Engineering, 1996, pp. 536–545. · DOI
- Korn, 1997, Efficiently supporting ad hoc queries in large datasets of time sequences, ACM SIGMOD Record, 26, 289, 10.1145/253262.253332 · DOI
- Portet, 2009, Automatic generation of textual summaries from neonatal intensive care data, Artif. Intell., 173, 789, 10.1016/j.artint.2008.12.002 · DOI
- Y. Cai and R. Ng, Indexing spatio-temporal trajectories with Chebyshev polynomials, in: Procedings of 2004 ACM SIGMOD International, 2004, p. 599. · DOI
- E. Bingham, Random projection in dimensionality reduction: applications to image and text data, in: Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001, pp. 245–250. · DOI
- Q. Chen, L. Chen, X. Lian, Y. Liu, Indexable PLA for efficient similarity search, in: Proceedings of the 33rd International Conference on Very large Data Bases, 2007, pp. 435–446.
- D. Minnen, T. Starner, M. Essa, C. Isbell, Discovering characteristic actions from on-body sensor data, in: Proceedings of 10th IEEE International Symposium on Wearable Computers, 2006, pp. 11–18. · DOI · Paper page
- Minnen, 2007, Discovering multivariate motifs using subsequence density estimation and greedy mixture learning, Proc. Natl. Conf. Artif. Intell., 22, 615
- A. Panuccio, M. Bicego, and V. Murino, A Hidden Markov Model-based approach to sequential data clustering, in Structural, Syntactic, and Statistical Pattern Recognition, T. Caelli, A. Amin, R. Duin, R. De, and M. Kamel, Eds. 2002. · DOI
- Kumar, 2005, Time-series bitmaps: a practical visualization tool for working with large time series databases, SIAM 2005 Data Min, 531, 10.1137/1.9781611972757.55 · DOI
- Corduas, 2008, Time series clustering and classification by the autoregressive metric, Comput. Stat. Data Anal., 52, 1860, 10.1016/j.csda.2007.06.001 · DOI
- K. Kalpakis, D. Gada, V. Puttagunta, Distance measures for effective clustering of ARIMA time-series, in: Proceedings 2001 IEEE International Conference on Data Mining, 2001, pp. 273–280. · DOI
- Agrawal, 1993, Efficient similarity search in sequence databases, Found. Data Organ. Algorithms, 46, 69, 10.1007/3-540-57301-1_5 · DOI
- K. Kawagoe, T. Ueda, A similarity search method of time series data with combination of Fourier and wavelet transforms, in: Proceedings Ninth International Symposium on Temporal Representation and Reasoning, 2002, 86–92. · DOI
- F.L. Chung, T.C. Fu, R. Luk, Flexible time series pattern matching based on perceptually important points, in: Jt. Conference on Artificial Intelligence Workshop, 2001, pp. 1–7.
- E. Keogh, S. Lonardi, C. Ratanamahatana, Towards parameter-free data mining, in: Proceedings of Tenth ACM SIGKDD International Conference on Knowledge Discovery Data Mining, vol. 22, no. 25, 2004, pp. 206–215. · DOI
- Bagnall, 2005, Clustering time series with clipped data, Mach. Learn., 58, 151, 10.1007/s10994-005-5825-6 · DOI
- Aref, 2004, Incremental, online, and merge mining of partial periodic patterns in time-series databases, Trans. Knowl. Data Eng, 16, 332, 10.1109/TKDE.2003.1262186 · DOI
- S. Chu, E. Keogh, D. Hart, M. Pazzani, et al., Iterative deepening dynamic time warping for time series, in: Proceedings of the Second SIAM International Conference on Data Mining, 2002, pp. 195–212. · DOI
- C. Ratanamahatana, E. Keogh, Three myths about dynamic time warping data mining, in: Proceedings of the International Conference on Data Mining (SDM’05), 2005, pp. 506–510. · DOI
- Smyth, 1997, Clustering sequences with hidden Markov models,, Adv. Neural Inf. Process. Syst, 9, 648
- Xiong, 2002, Mixtures of ARMA models for model-based time series clustering, Data Min, 2002. ICDM, 2003, 717
- Sakoe, 1971, A dynamic programming approach to continuous speech recognition,, Proceedings of the Seventh International Congress on Acoustics, vol. 3, 65
- M. Vlachos, G. Kollios, D. Gunopulos, Discovering similar multidimensional trajectories, in: Proceedingsof 18th International Conference on Data Engineering, 2002, pp. 673–684. · DOI
- A. Banerjee, J. Ghosh, Clickstream clustering using weighted longest common subsequences, in: Proceedings of the Workshop on Web Mining, SIAM Conference on Data Mining, 2001, pp. 33–40.
- Latecki, 2005, Elastic partial matching of time series,, Knowl. Discov. Databases PKDD 2005, 577, 10.1007/11564126_60 · DOI
- Keogh, 2007, Compression-based data mining of sequential data,, Data Min. Knowl. Discov., 14, 99, 10.1007/s10618-006-0049-3 · DOI
- Rodgers, 1988, Thirteen ways to look at the correlation coefficient,, Am. Stat, 42, 59, 10.2307/2685263 · DOI
- P. Indyk, N. Koudas, Identifying representative trends in massive time series data sets using sketches, in: Proceedings of 26th International Conference on Very Large Data Bases, 2000, pp. 363–372.
- Yka Huhtala ; Juha Karkkainen and Hannu T. Toivonen "Mining for similarities in aligned time series using wavelets", Proc. SPIE 3695, Data Mining and Knowledge Discovery: Theory, Tools, and Technology, 150 (February 25, 1999); doi:10.1117/12.339977; http://dx.doi.org/10.1117/12.339977. · DOI
- Last, 2004, Data mining in time series databases, World Sci.
- Z. Zhang, K. Huang, T. Tan, Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes, in: Proceedings of 18th International Conference on Pattern Recognition, ICPR 2006, vol. 3, pp. 1135–1138, 2006. · DOI
- Aach, 2001, Aligning gene expression time series with time warping algorithms, Bioinformatics, 17, 495, 10.1093/bioinformatics/17.6.495 · DOI
- Dahlhaus, 1996, On the Kullback–Leibler information divergence of locally stationary processes, Stoch. Process. Appl, 62, 139, 10.1016/0304-4149(95)00090-9 · DOI
- E. Keogh, A probabilistic approach to fast pattern matching in time series databases, in: Proceedings of the 3rd International Conference of Knowledge Discovery and Data Mining, 1997, pp. 52–57.
- Golay, 1998, A new correlation-based fuzzy logic clustering algorithm for FMRI,, Magn. Reson. Med., 40, 249, 10.1002/mrm.1910400211 · DOI
- Wang, 2000, Supporting content-based searches on time series via approximation,, Sci Stat Database, 69, 10.1109/SSDM.2000.869779 · DOI
- L. Chen R. Ng, On the marriage of lp-norms and edit distance, in: Proceedings of the Thirtieth International Conference on Very Large Data Bases-Volume 30, 2004, pp. 792–803. · DOI
- L. Chen, M.T. Özsu, V. Oria, Robust and fast similarity search for moving object trajectories, in: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, 2005, pp. 491–502. · DOI
- Chen, 2005, Using multi-scale histograms to answer pattern existence and shape match queries,, Time, 2, 217
- Aßfalg, 2006, Similarity search on time series based on threshold queries, Adv. Database Technol. 2006, 276, 10.1007/11687238_19 · DOI
- E. Frentzos, K. Gratsias, Y. Theodoridis, Index-based most similar trajectory search, in: Proceedings of 23rd International Conference on Data Engineering, 2007, ICDE 2007. IEEE, 2007, pp. 816–825. · DOI
- M.D. Morse, J.M. Patel, An efficient and accurate method for evaluating time series similarity, in: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data SIGMOD 07, 2007, p. 569. · DOI
- Y. Chen, M.A. Nascimento, B.C. Ooi,A.K.H. Tung, Spade: on shape-based pattern detection in streaming time series, in: Proceedings of IEEE 23rd International Conference on Data Engineering, 2007. ICDE 2007. , 2007, pp. 786–795. · DOI
- Zhang, 2009, A notime series classificationvel pattern extraction method for, Optim. Eng., 10, 253, 10.1007/s11081-008-9056-0 · DOI
- Lang, 2010, Dictionary-based compression for long time-series similarity, Knowl. Data Eng. IEEE Trans, 22, 1609, 10.1109/TKDE.2009.201 · DOI
- Salvador, 2007, Toward accurate dynamic time warping in linear time and space, Intell. Data Anal., 11, 561, 10.3233/IDA-2007-11508 · DOI
- Itakura, 1975, Minimum prediction residual principle applied to speech recognition. Minimum prediction residual principle applied to speech recognition, IEEE Trans. Acoust. Speech Signal Process, 23, 67, 10.1109/TASSP.1975.1162641 · DOI
- B. Lkhagva, Y.u. Suzuki, K. Kawagoe, New time series data representation ESAX for financial applications, in: Proceedings of 22nd International Conference on Data Engineering Workshops, 2006, pp. 17–22. · DOI
- A. Corradini, Dynamic time warping for off-line recognition of a small gesture vocabulary, in: IEEE ICCV Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, 2001, pp. 82–89. · DOI
- Rabiner, 1979, Speaker-independent recognition of isolated words using clustering techniques,, IEEE Trans. Acoust. Speech Signal Process, 27, 336, 10.1109/TASSP.1979.1163259 · DOI
- Niennattrakul, 2007, Inaccuracies of shape averaging method using dynamic time warping for time series data, Comput. Sci. 2007, 513, 10.1007/978-3-540-72584-8_68 · DOI
- Kaufman, 1990, vol. 39
- Vuori, 2002, A comparison of techniques for automatic clustering of handwritten characters, Pattern Recognit., 2002, 3, 30168
- T.W. Liao, B. Bolt, J. Forester, E. Hailman, C. Hansen, R. Kaste, J. O’May, Understanding and projecting the battle state, in: Proceedings of 23rd Army Science Conference, Orlando, FL, 2002, pp. 2–3.
- Liao, 2006, An adaptive genetic clustering method for exploratory mining of feature vector and time series data,, Int. J. Prod. Res., 44, 2731, 10.1080/00207540600600130 · DOI
- Gupta, 1996, Nonlinear alignment and averaging for estimating the evoked potential, IEEE Trans. Biomed. Eng., 43, 348, 10.1109/10.486255 · DOI
- Caiani, 1998, Warped-average template technique to track on a cycle-by-cycle basis the cardiac filling phases on left ventricular volume,, Comput. Cardiol. 1998, 73
- Oates, 2001, Using dynamic time warping to bootstrap HMM-based clustering of time series, Seq. Learn. Paradig. ALGORITHMS, Appl, 1, 35
- W. Abdulla, D. Chow, Cross-words reference template for DTW-based speech recognition systems, in: TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region, vol. 4, 2003, pp. 1576–1579. · DOI
- Petitjean, 2011, A global averaging method for dynamic time warping, with applications to clustering, Pattern Recognit, 44, 678, 10.1016/j.patcog.2010.09.013 · DOI
- L. Bergroth, H. Hakonen, A survey of longest common subsequence algorithms, in: Proceedings of the Seventh International Symposium on String Processing and Information Retrieval, 2000. SPIRE 2000, 2000, pp. 39–48. · DOI
- S. Aghabozorgi, T.Y. Wah, A. Amini, M.R. Saybani, A new approach to present prototypes in clustering of time series, in: Proceedings of the 7th International Conference of Data Mining, vol. 28, no. 4, 2011, pp. 214–220.
- Aghabozorgi, 2012, Incremental clustering of time-series by fuzzy clustering, J. Inf. Sci. Eng., 28, 671
- Karypis, 1999, Chameleon: hierarchical clustering using dynamic modeling, Comput. (Long. Beach. Calif)., 32, 68
- Guha, 1998, CURE: an efficient clustering algorithm for large databases,, ACM SIGMOD Rec., 27, 73, 10.1145/276305.276312 · DOI
- Vlachos, 2003, A wavelet-based anytime algorithm for k-means clustering of time series, Proc. Work. Clust, 23
- J.J. Van Wijk and E.R. Van Selow, Cluster and calendar based visualization of time series data, in: Proceedings of 1999 IEEE Symposium on Information Vision, 1999, pp. 4–9. · DOI
- T. Oates, M.D. Schmill, P.R. Cohen, A method for clustering the experiences of a mobile robot that accords with human judgments, in: Proceedings of the National Conference on Artificial Intelligence, 2000, pp. 846–851.
- Hirano, 2005, Empirical comparison of clustering methods for long time-series databases, Act. Min, 3430, 268, 10.1007/11423270_15 · DOI
- J. MacQueen, Some methods for classification and analysis of multivariate observations, in: Proceedings of the fifth Berkeley symposium Mathematical Statist. Probability, vol. 1, 1967, pp. 281–297.
- R.T. Ng, J. Han, Efficient and effective clustering methods for spatial data mining, in: Proceedings of the International Conference on Very Large Data Bases, 1994, pp. 144–144.
- U. Fayyad, C. Reina, P.S. Bradley, Initialization of iterative refinement clustering algorithms, in: Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, 1998, pp. 194–198.
- Bradley, 1998, Scaling clustering algorithms to large databases, Knowl. Discov. Data Min, 9
- Beringer, 2006, Online clustering of parallel data streams, Data Knowl. Eng., 58, 180, 10.1016/j.datak.2005.05.009 · DOI
- Dunn, 1973, A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, Cybern. Syst., 3, 32
- Krishnapuram, 2001, “Low-complexity fuzzy relational clustering algorithms for web mining,”, Fuzzy Syst. IEEE Trans, vol. 9, 595, 10.1109/91.940971 · DOI
- Dembélé, 2003, Fuzzy C-means method for clustering microarray data, Bioinformatics, 19, 973, 10.1093/bioinformatics/btg119 · DOI
- J. Alon, S. Sclaroff, Discovering clusters in motion time-series data in: Proceedings of Computer Society Conference on Computer Vision and Pattern Recognition, 2003, pp. 375–381. · DOI
- Fisher, 1987, Knowledge acquisition via incremental conceptual clustering, Mach. Learn., 2, 139, 10.1007/BF00114265 · DOI
- Kohonen, 1990, The self-organizing map, Proc. IEEE, 78, 1464, 10.1109/5.58325 · DOI · Paper page
- Biernacki, 2000, Assessing a mixture model for clustering with the integrated completed likelihood, IEEE Trans. Pattern Anal. Mach. Intell, 22, 719, 10.1109/34.865189 · DOI
- Bicego, 2003, Similarity-based clustering of sequences using hidden Markov models, Mach. Learn. data Min. pattern Recognit, 2734, 95
- J. Hu, B. Ray, L. Han, An interweaved hmm/dtw approach to robust time series clustering, in: Proceedings of 18th International Conference on Pattern Recognition, 2006. ICPR 2006, , vol. 3, 2006, pp. 145–148. · DOI
- Andreopoulos, 2009, , A roadmap of clustering algorithms: finding a match for a biomedical application, Brief. Bioinform., 10, 297, 10.1093/bib/bbn058 · DOI
- Ester, 1996, A density-based algorithm for discovering clusters in large spatial databases with noise, In Kdd, 96, 226
- Ankerst, 1999, OPTICS: Ordering points to identify the clustering structure, ACM SIGMOD Rec, 28, 40, 10.1145/304181.304187 · DOI
- S. Chandrakala, C. Chandra, A density based method for multivariate time series clustering in kernel feature space, in: Proceedings of IEEE International Joint Conference on Neural Networks IEEE World Congress on Computational Intelligence, vol. 2008, 2008, pp. 1885–1890. · DOI
- W. Wang, J. Yang, R. Muntz, STING: a statistical information grid approach to spatial data mining, in: Proceedings of the International Conference on Very Large Data Bases, 1997, pp. 186–195.
- G. Sheikholeslami, S. Chatterjee, A. Zhang, Wavecluster: A multi-resolution clustering approach for very large spatial databases, in: proceedings of the International conference on Very Large Data Bases, 1998, pp. 428–439.
- Kakizawa, 1998, Discrimination and clustering for multivariate time series, J. Am. Stat. Assoc, 93, 328, 10.1080/01621459.1998.10474114 · DOI
- Policker, 2000, Nonstationary time series analysis by temporal clustering, Syst. Man, Cybern. Part B, 30, 339, 10.1109/3477.836381 · DOI
- Qian, 2001, Beyond synexpression relationships: local clustering of time-shifted and inverted gene expression profiles identifies new, biologically relevant interactions1, J. Mol. Biol., 314, 1053, 10.1006/jmbi.2000.5219 · DOI
- Wang, 2004, Joint segmentation and classification of time series using class-specific features, Syst. Man, Cybern. Part B Cybern. IEEE Trans, 34, 1056, 10.1109/TSMCB.2003.819486 · DOI
- Wang, 2005, Dimension reduction for clustering time series using global characteristics, Comput. Sci. 2005, 792, 10.1007/11428862_108 · DOI
- J. Abonyi, B. Feil, S. Nemeth, P. Arva, Principal component analysis based time series segmentation-application to hierarchical clustering for multivariate process data, in: Proceedings of IEEE International Conference on Computational Cybernetics, 2005, pp. 29–31.
- Tseng, 2005, Efficiently mining gene expression data via a novel parameterless clustering method,, IEEE/ACM Trans. Comput. Biol. Bioinforma, 2, 355, 10.1109/TCBB.2005.56 · DOI
- Bao, 2007, A generalized model for financial time series representation and prediction,, Appl. Intell., 29, 1, 10.1007/s10489-007-0063-1 · DOI
- Bao, 2008, Intelligent stock trading system by turning point confirming and probabilistic reasoning, Exp. Syst. Appl., 34, 620, 10.1016/j.eswa.2006.09.043 · DOI
- W. Liu, L. Shao, Research of SAX in distance measuring for financial time series data, in: Proceedings of the First International Conference on Information Science and Engineering, 2009, no. 70572070, pp. 935–937. · DOI
- T.C. Fu, F.L. Chung, R. Luk, C.M. Ng, Financial time series indexing based on low resolution clustering, in: Proceedings of the 4th IEEE International Conference on Data Mining (ICDM-2004), 2010, pp. 5–14.
- Lai, 2010, A novel two-level clustering method for time series data analysis,, Expert Syst. Appl., 37, 6319, 10.1016/j.eswa.2010.02.089 · DOI
- Zhang, 2011, A novel clustering method on time series data, Expert Syst. Appl., 38, 11891, 10.1016/j.eswa.2011.03.081 · DOI
- J. Zakaria, A. Mueen, E. Keogh, Clustering time series using unsupervised-shapelets, in: Proceedings of 2012 IEEE 12th International Conference on Data Mining, 2012, pp. 785–794. · DOI
- Darkins, 2013, Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm, PLoS One, 8, e59795, 10.1371/journal.pone.0059795 · DOI
- Seref, 2014, Mathematical programming formulations and algorithms for discrete k-median clustering of time-series data, INFORMS J. Comput., 26, 160, 10.1287/ijoc.2013.0554 · DOI
- Ghassempour, 2014, Clustering multivariate time series using hidden Markov models, Int. J. Environ. Res. Public Health, 11, 2741, 10.3390/ijerph110302741 · DOI
- Aghabozorgi, 2014, A hybrid algorithm for clustering of time series data based on affinity search technique, Sci.World J., 2014, 562194, 10.1155/2014/562194 · DOI
- A. Bellaachia, D. Portnoy, Y. Chen, A.G. Elkahloun, E-CAST: a data mining algorithm for gene expression data, in: Workshop on Data Mining in Bioinformatics, 2002, pp. 49–54.
- Lin, 2005, A MPAA-based iterative clustering algorithm augmented by nearest neighbors search for time-series data streams, Adv. Knowl. Discov. Data Min, 333, 10.1007/11430919_40 · DOI
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Time Series Clustering with Topological and Geometric Mixed Distance
Zhang, Y., Shi, Q., Zhu, J., Peng, J., & Li, H.-F. (2021) · Mathematics · doi:10.3390/math9091046 · Cited by 9
“In this framework, time series clustering is an important method for understanding the intrinsic properties of temporal data [1].”
BackgroundMethodInfluentialMethods and measures to quantify ICU patient heterogeneity
Cuadrado, D., Riaño, D., Gómez, J., Rodríguez, A., & Bodí, M. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103768 · Cited by 9
Characterizing an epidemiological geography of the United States: influenza as a case study
Rosensteel, G. E., Lee, E. C., Colizza, V., & Bansal, S. (2021) · medRxiv · doi:10.1101/2021.02.24.21252361 · Cited by 9
“Shape-based clustering approaches, such as ours, are expected to be more effective compared to feature-based approaches as they incorporate information about the entire time series, and can be used across different times and scales [42].”
MethodBlockchain mechanism and distributional characteristics of cryptos
Lin, M.-B., Khowaja, K., Chen, C. Y., & Härdle, W. (2020) · Social Science Research Network · doi:10.2139/ssrn.3784776 · Cited by 9
“For the clustering problems (Aghabozorgi et al., 2015), defining the distance metric between points in time series with various lengths is not conventional.”
BackgroundFault detection of continuous glucose measurements based on modified k-medoids clustering algorithm
Yu, X., Sun, X., Zhao, Y., Liu, J.-C., & Li, H.-R. (2020) · Neural computing & applications (Print) · doi:10.1007/s00521-020-05432-2 · Cited by 9
A Practical Evaluation of Dynamic Time Warping in Financial Time Series Clustering
Puspita, P. E., & Zulkarnain (2020) · International Conference on Advanced Computer Science and Information System · doi:10.1109/icacsis51025.2020.9263123 · Cited by 9
“In general, clustering is usually utilized as a preliminary part of other tasks [14].”
BackgroundMethodInfluentialHan, Y.-N., & He, Z. (2020) · doi:10.1155/2020/8810753 · Cited by 9
“Purity is an external index that is used to measure the similarity of the formed clusters to external clusters such as ground truth [22], considering G � G1, G2, .”
Forecasting multiple-well flow rates using a novel space-time modeling approach
Ahmadi, R., Shahrabi, J., & Aminshahidy, B. (2020) · Journal of Petroleum Science and Engineering · doi:10.1016/j.petrol.2020.107027 · Cited by 9
Trinh, H. T., Dhar, B. D., Simioni, M., de Haan, S., Huynh, T. T. T., Huynh, T. V., & Jones, A. D. (2020) · Frontiers in Sustainable Food Systems · doi:10.3389/fsufs.2020.00015 · Cited by 11
“The supermarket enumeration data were subjected to time series clustering analysis (Aghabozorgi et al., 2015).”
MethodMultivariate Time Series Clustering and its Application in Industrial Systems
Sürmeli, B., & Tümer, M. (2019) · Cybernetics and systems · doi:10.1080/01969722.2019.1691851 · Cited by 9
Ochodek, M., Staron, M., & Meding, W. (2019) · Information and Software Technology · doi:10.1016/j.infsof.2019.06.003 · Cited by 9
Zhao, X., Hu, C., Liu, Z., & Meng, Y.-Y. (2019) · ISPRS Int. J. Geo Inf. · doi:10.3390/ijgi8060281 · Cited by 9
“In such a process, the time series are grouped based on a certain similarity measure [9].”
BackgroundTime Series Data Mining with an Application to the Measurement of Underwriting Cycles
Owadally, I., Zhou, F., Otunba, R., Lin, J., & Wright, D. (2019) · North American Actuarial Journal · doi:10.1080/10920277.2019.1570468 · Cited by 9
An application of hierarchical Gaussian processes to the detection of anomalies in star light curves
Twomey, N., Chen, H., Diethe, T., & Flach, P. A. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.11.087 · Cited by 9
“Not all anomaly detection techniques necessarily fall in the remit of probabilistic models (including dynamic time warping [10], neural networks [11], grammar mining [12], max-margin methods [13]) and the interested reader is referred to [14, 15] for overviews of such methods.”
BackgroundDataSeg: dynamic streaming sensor data segmentation for activity recognition
Sfar, H., & Bouzeghoub, A. (2019) · ACM Symposium on Applied Computing · doi:10.1145/3297280.3297332 · Cited by 9
Clustering the occupant behavior in residential buildings: a method comparison
Carbonare, N., Pflug, T., & Wagner, A. (2018) · Bauphysik · doi:10.1002/bapi.201800023 · Cited by 9
“Several pre-processing methods are presented in the former literature [7, 8, 13].”
MethodActivity Archetypes in Question-and-Answer (Q8A) Websites—A Study of 50 Stack Exchange Instances
Santos, T., Walk, S., Kern, R., Strohmaier, M., & Helic, D. (2017) · ACM Transactions on Social Computing · doi:10.1145/3301612 · Cited by 9
“We select time series features and apply Euclidean K-Means on them to cope with the challenge that discrete valued time series data presents and which is, according to Aghabozorgi et al. [2], rarely dealt with in time series clustering literature.”
MethodPan, Y.-Q., Nie, Y., Watene, C., Zhu, J., & Liu, F. (2017) · Remote Sensing · doi:10.3390/rs9040374 · Cited by 9
“Time series similarity/dissimilarity is a key research issue in the data mining community [33].”
BackgroundDurán-Rosal, A. M., de la Paz-Marín, M., Gutiérrez, P. A., & Hervás-Martínez, C. (2017) · Neural Processing Letters · doi:10.1007/s11063-017-9592-8 · Cited by 9
Feature-driven Time Series Clustering
Tiano, D., Bonifati, A., & Ng, R. T. (2021) · International Conference on Extending Database Technology · doi:10.5441/002/edbt.2021.33 · Cited by 9
“Among the unsupervised clustering algorithms [1], kShape[17] can be considered as the state of the art algorithm.”
BURST: Rendering Clustering Techniques Suitable for Evolving Streams
Giannoulidis, A., Gounaris, A., & Paparrizos, J. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3749646.3749675 · Cited by 8
“Although extensive research has advanced static time-series clustering methods [2, 20, 25, 32, 37, 42, 47, 50, 51], online time-series clustering [58], remains significantly under-explored despite its practical importance on real-world problems.”
Recovering urban nightlife: COVID-19 insights from Google Places activity trends in Madrid
Santiago-Iglesias, E., Romanillos, G., Carpio-Pinedo, J., Sun, W.-Z., & García-Palomares, J. (2024) · Journal of maps · doi:10.1080/17445647.2024.2371927 · Cited by 8
“The Time Series Clustering method identifies the locations in a Space-Time Cube (STC) that are most similar and partitions them into distinct clusters in which members of each cluster have similar time series characteristics (Aghabozorgi et al., 2015).”
Ink and Algorithm: Exploring Temporal Dynamics in Human-AI Collaborative WritingPreprint
Yang, K.-X., Cheng, Y.-X., Zhao, L.-X., Raković, M., Swiecki, Z., Gašević, D., & Chen, G.-L. (2024) · arXiv.org · doi:10.48550/arxiv.2406.14885 · Cited by 8
“In our case, we opted for K-means for clustering purposes, a widely recognized method for clustering time-series data (Aghabozorgi et al., 2015).”
MethodYamamoto, K., Sakaguchi, M., Ōnishi, A., Yokoyama, S., Matsui, Y., Yamamoto, W., Onizawa, H., Fujii, T., Murata, K., Tanaka, M., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0302308 · Cited by 6
A Trend-Granulation-Based Fuzzy C-Means Algorithm for Clustering Interval-Valued Time Series
Yang, Z., Yu, F., Pedrycz, W., Yang, H., Tang, Y.-Q., & Ouyang, C. (2024) · IEEE transactions on fuzzy systems · doi:10.1109/tfuzz.2023.3321921 · Cited by 8
“Similar to the shape-based numerical time-series clustering methods, four aspects, namely representation, distance measure, clustering prototypes, and clustering method, need to be considered in the shape-based ITS clustering [10].”
Chen, Y., Obrecht, C., & Kuznik, F. (2024) · Integr. Comput. Aided Eng. · doi:10.3233/ica-230731 · Cited by 8
“Because of its ability to measure the similarities between curves, DTW has been mainly used for clustering algorithms [20], but less for regression because of its non-differentiability for gradient descent optimization.”
ShapeDBA: Generating Effective Time Series Prototypes using ShapeDTW Barycenter Averaging
Ismail-Fawaz, A., Fawaz, H. I., Petitjean, F., Devanne, M., Weber, J., Berretti, S., Webb, G. I., & Forestier, G. (2023) · AALTD@ECML/PKDD · doi:10.48550/arxiv.2309.16353 · Cited by 8
“Clustering for time series data has been very much addressed in the literature [13,1].”
BackgroundDynamic Prediction of Delays in Software Projects using Delay Patterns and Bayesian Modeling
Kula, E., Greuter, E., van Deursen, A., & Gousios, G. (2023) · ESEC/SIGSOFT FSE · doi:10.1145/3611643.3616328 · Cited by 8
“This approach has been shown to be appropriate for short time series [3].”
BackgroundTowards the use of Unsupervised Causal Learning in Wireless Networks Operation
Sousa, M., Vieira, P. A., Queluz, M. P., & Rodrigues, A. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101764 · Cited by 8
“Multivariate Time Series Clustering: Clustering multivariate time series is generally performed using one of three approaches: shaped-based, feature-based, or model-based (Aghabozorgi et al., 2015).”
Guan, B., Zhao, C., Yuan, X., Long, J., & Li, X. (2023) · The Journal of Supercomputing · doi:10.1007/s11227-023-05562-z · Cited by 7
Gutiérrez, A. P., Delfa, J. L. V., & López, M. V. (2023) · Environmental and Ecological Statistics · doi:10.1007/s10651-023-00572-9 · Cited by 8
“Since TS clustering is used to identify interesting patterns in TS data sets, finding TS clusters can be valuable in different domains, such as responding to anomalies or novelties, detecting discordance, integrating applications in dynamic change recognition in TS, and predicting, recommending, and discovering patter…”
BackgroundDissimilarity-Preserving Representation Learning for One-Class Time Series Classification
Mauceri, S., Sweeney, J. S., Nicolau, M., & McDermott, J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3273503 · Cited by 9
“The analysis of the dissimilarity between time series is fundamental for time series classification [4], but also for several other time series mining tasks like clustering [5], indexing [6], motif discovery [7], etc.”
BackgroundPérez, S. I., Moral-Rubio, S., & Criado, R. (2022) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2022.128306 · Cited by 8
AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series Clustering
Li, G., Choi, B., Xu, J.-L., Bhowmick, S., Mah, D., & Wong, G. (2022) · arXiv.org · doi:10.48550/arxiv.2208.04313 · Cited by 8
“Interested readers may refer to some excellent review papers on time series clustering [1], [25].”
BackgroundAlsulami, S., & Bayati, J. H. (2022) · Journal of chemistry · doi:10.1155/2022/1726286 · Cited by 8
“Given that Pearson’s correlation is invariant to scale measure [7], countries that had similar shapes at their trajectories of propagation but differ in the proportion of the affected population will be considered similar and are likely to cluster.”
BackgroundRetractedA Multivariate Approach for Spatiotemporal Mobile Data Traffic Prediction
Shawel, B. S., Mare, E., Debella, T. T., Pollin, S., & Woldegebreal, D. H. (2022) · ITISE 2022 · doi:10.3390/engproc2022018010 · Cited by 8
An Ensemble Clustering Approach (Consensus Clustering) for High-Dimensional Data
Yan, J.-D., & Liu, W. (2022) · Security and Communication Networks · doi:10.1155/2022/5629710 · Cited by 8
RetractedLin, Y.-J., Roa-Ureta, R., Basali, A. U., Alcaria, J., Lindo, R. T., Qurban, M., Prihartato, P. K., Qasem, A., & Rabaoui, L. (2021) · Coral reefs · doi:10.1007/s00338-021-02181-z · Cited by 8
Automatic subject-specific spatiotemporal feature selection for subject-independent affective BCI
Almarri, B. A., Rajasekaran, S., & Huang, C.-H. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253383 · Cited by 7
“mutual information are reportedly used on such data [23].”
BackgroundRSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach
Madani, P., & Vlajic, N. (2021) · Journal of Cybersecurity and Privacy · doi:10.3390/jcp1030023 · Cited by 8
“…environment, and as a result the variance of the RSSI time-series changes significantly when occupants are present and move around the property—we call this effect time-series clustering [9] (refer to Figure 1 where there are two different clusters, one with lower volatility than the other).”
BackgroundNémethová, J., & Rybanský, Ľ. (2021) · Sustainability · doi:10.3390/su13158512 · Cited by 8
“’s [44], showed that no best index of internal cluster validity exists.”
BackgroundComputational methods for characterizing and learning from heterogeneous cell signaling data
Kinnunen, P. C., Luker, K. E., Luker, G. D., & Linderman, J. J. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.04.009 · Cited by 8
Herrera, M., & Godoy‐Faúndez, Á. (2021) · Future Internet · doi:10.3390/fi13050112 · Cited by 3
“The rationale behind DTW is, given two time series, to stretch or compress them locally in order to make one resemble the other as much as possible [7].”
BackgroundDETECT: A Hierarchical Clustering Algorithm for Behavioural Trends in Temporal Educational Data
McBroom, J., Yacef, K., & Koprinska, I. (2020) · International Conference on Artificial Intelligence in Education · doi:10.1007/978-3-030-52237-7_30 · Cited by 8
Nonlinear Time Series Classification Using Bispectrum-based Deep Convolutional Neural Networks
Parker, P. A., Holan, S., & Ravishanker, N. (2020) · Applied Stochastic Models in Business and Industry · doi:10.1002/asmb.2536 · Cited by 8
“Aghabozorgi et al. (2015) give a review of many modern approaches to time series clustering.”
BackgroundDynamic time alignment kernel-based fuzzy clustering of non-equal length vector time series
Guo, H.-Y., Wang, L.-D., & Liu, X. (2019) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-019-01007-3 · Cited by 8
Clustering discrete-valued time series
Roick, T., Karlis, D., & McNicholas, P. (2019) · Advances in Data Analysis and Classification · doi:10.1007/s11634-020-00395-7 · Cited by 8
Villalobos, K., Diez, B., Illarramendi, A., Goñi, A., & Blanco, J. (2018) · International Conference on Information and Knowledge Management · doi:10.1145/3269206.3269213 · Cited by 8
“However, we have focused our work on time-series dimensionality reduction techniques instead of general purpose data compression techniques because, although both techniques can be used to reduce data storage and transmission costs, dimensionality reduction techniques also allow to optimize some data analysis processe…”
MethodDohnal, M. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.07.029 · Cited by 8
Dependency-Aware Clustering of Time Series and Its Application on Energy Markets
Ruiz-Abellón, M. D. C., Gabaldón, A., & Guillamón, A. (2016) · doi:10.3390/en9100809 · Cited by 8
Energy State Sensing for Robust MAC Protocol Identification in Underwater Acoustic Networks
Ma, G.-Y., Shen, X.-H., Yan, Y., Yao, H.-Y., & Wang, H. (2026) · IEEE Transactions on Cognitive Communications and Networking · doi:10.1109/tccn.2025.3561297 · Cited by 8
“Given that the energy values after noise reduction cluster around a finite set of state values, each corresponding to a different reception status, we can automatically categorize similar energy values using a data-driven one-dimensional clustering method [34].”
Schaffer, M., Vera‐Valdés, J. E., & Marszal-Pomianowska, A. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123586 · Cited by 13
Gross, J., Buettner, R., & Baumgartl, H. (2022) · IEEE Access · doi:10.1109/access.2022.3148711 · Cited by 10
“In addition, our outcome matches the findings of previous literature reviews and surveys regarding dominant subject areas in time-series analysis [18], [52], [56].”
ResultDeep Learning for Skeleton-Based Human Activity Segmentation: An Autoencoder Approach
Hossen, M. A., Naim, A. G., & Abas, P. E. (2024) · Technologies · doi:10.3390/technologies12070096 · Cited by 7
“This visualization is structured across two distinct rows; the upper row showcases a series of skeleton frames performing varied 32], is adopted, reflecting a strategy consistent with approaches used in previous works for segmenting accelerometer-based human activity data [33,34].”
MethodLuque-Sánchez, Á., Díaz-Cabrera, J. M., Galvín, A. P., Gámez-Granados, J. C., & Castillejo-González, I. (2025) · Journal of Environmental Management · doi:10.1016/j.jenvman.2025.127439 · Cited by 7
Interpretable clustering of PS-InSAR time series for ground deformation detection
Masciulli, C., Guiduzzi, G., Tiano, D., Zocchi, M., Guerra, F., Mazzanti, P., & Mugnozza, G. S. (2025) · Computational Geosciences · doi:10.1016/j.cageo.2025.105959 · Cited by 7
ElHaj, K., & Alshamsi, D. (2025) · Journal of Big Data · doi:10.1186/s40537-025-01060-6 · Cited by 7
“Time-Series Clustering (TSC) is a mature research field with applications in various scientific fields [3].”
Measuring unit relevance and stability in hierarchical spatio-temporal clustering
Cerqueti, R., & Mattera, R. (2025) · Spatial Statistics · doi:10.1016/j.spasta.2025.100880 · Cited by 7
Asha, A., Rajeshkumar, L., Pandi, V., Shobana, D., J, L., & Dayanidhy, M. (2024) · 2024 Global Conference on Communications and Information Technologies (GCCIT) · doi:10.1109/gccit63234.2024.10862745 · Cited by 7
Mining Spatiotemporal Mobility Patterns Using Improved Deep Time Series Clustering
Zhang, Z., Li, D., Zhang, Z., & Duffield, N. G. (2024) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi13110374 · Cited by 6
Utilizing echocardiography and unsupervised machine learning for heart failure risk identification.
Simonsen, J., Modin, D., Skaarup, K., Djernæs, K., Lassen, M., Johansen, N., Marott, J., Jensen, M. T., Jensen, G., Schnohr, P., et al. (2024) · International Journal of Cardiology · doi:10.1016/j.ijcard.2024.132636 · Cited by 7
Application of time-series analysis to urban climate change assessment
Liu, H., Li, M., Yang, C., & Jia, L. (2024) · International Journal of Environmental Science and Technology · doi:10.1007/s13762-024-05961-6 · Cited by 7
“It has been extensively applied in domains like regional/global climate, biology, and finance (Gorji Sefidmazgi et al. 2014; Aghabozorgi et al. 2015) for exploratory analysis, and was only recently introduced to urban climatology for change assessments of UHI intensity (UHII) (Liu et al. 2018,…”
Statistical jump model for mixed-type data with missing data imputation
Cortese, F. P., & Pievatolo, A. (2024) · Advances in Data Analysis and Classification · doi:10.1007/s11634-025-00631-y · Cited by 7
“Clustering mixed-type data is particularly challenging when accounting for its temporal evolution (Aghabozorgi et al. 2015).”
Dependence-Based Fuzzy Clustering of Functional Time Series
López-Oriona, Á., Sun, Y., & Shang, H.-L. (2024) · Journal of Computational And Graphical Statistics · doi:10.1080/10618600.2025.2489537 · Cited by 7
“For comprehensive reviews, see Aghabozorgi, Shirkhorshidi, and Wah (2015) and Maharaj, D’Urso, and Caiado (2019).”
Göttel, V., Lichtinger, Y., & Engelen, A. (2024) · Long range planning · doi:10.1016/j.lrp.2024.102427 · Cited by 7
Erkuş, E. C., & Purutçuoğlu, V. (2023) · Journal of Computer Science · doi:10.2139/ssrn.4349068 · Cited by 7
Identifying Rural High Energy Intensity Residential Buildings Using Metered Data
Koupaei, D. M., Cetin, K. S., Passe, U., Kimber, A., & Poleacovschi, C. (2023) · Energy and Buildings · doi:10.1016/j.enbuild.2023.113604 · Cited by 7
Ahir, R. K., & Chakraborty, B. (2023) · Energy · doi:10.1016/j.energy.2023.128939 · Cited by 7
Scalable Gromov–Wasserstein Based Comparison of Biological Time Series
Kravtsova, N., McGee, R. L., II, & Dawes, A. T. (2023) · Bulletin of Mathematical Biology · doi:10.1007/s11538-023-01175-y · Cited by 7
“A distance measure is required for any type of machine learning task that quantitatively compares time series (Esling and Agon 2012), such as time series clas-sification (Abanda et al. 2019) or clustering (Aghabozorgi et al. 2015).”
BackgroundA time series analysis of milk productivity in US dairy states.
Li, M., Reed, K., & Cabrera, V. E. (2023) · Journal of Dairy Science · doi:10.3168/jds.2022-22751 · Cited by 7
Volke, J., Reit, M., & Heim, H. (2023) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2023.03.072 · Cited by 7
Time series clustering based on prediction accuracy of global forecasting models
López-Oriona, Á., Montero-Manso, P., & Fernández, J. A. V. (2023) · arXiv.org · doi:10.48550/arxiv.2305.00473 · Cited by 7
“Comprehensive overviews on the topic are provided in [1, 2, 3, 4, 5].”
BackgroundIntroducing Autonomous Shuttle Services Based on Travel Patterns for the Elderly
Kang, E., Park, S., Seo, Y., & Kim, H. (2023) · Journal of Advanced Transportation · doi:10.1155/2023/2206625 · Cited by 9
“As the number of data increases, the computational burden rapidly increases, so large datasets cannot be effectively processed in this manner [36].”
Lambert, J., Leutenegger, A., Jannot, A., & Baudot, A. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104309 · Cited by 7
Sousa, E. R. D., Hipsey, M., & Vogwill, R. (2023) · Frontiers in Earth Science · doi:10.3389/feart.2022.886304 · Cited by 7
“, 2014), time series clustering methods (Aghabozorgi et al., 2015) and ensemble machine learning techniques (Zounermat-Kermani et al.”
BackgroundTemporal dynamics clustering for analyzing cell behavior in mobile networks
Li, S., Francini, G., & Magli, E. (2023) · Comput. Networks · doi:10.1016/j.comnet.2023.109578 · Cited by 7
xPM: Enhancing exogenous data visibility
Banham, A., Leemans, S., Wynn, M., Andrews, R., Laupland, K., & Shinners, L. (2022) · Artif. Intell. Medicine · doi:10.1016/j.artmed.2022.102409 · Cited by 7
Yang, Z., & Gao, W. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19159596 · Cited by 10
“In this process, the center of mass of each cluster changes until the Euclidean distance between the elements of the clusters and the center of mass is minimized to select the optimal number of clusters, and the most reasonable number of clusters is determined when the Sum of Squared Errors (SSE) is sufficiently low a…”
BackgroundIntelligent Model for Power Cells State of Charge Forecasting in EV
López, V., Jove, E., Gato, F. Z., Pinto-Santos, F., Piñón-Pazos, A. J., Casteleiro‐Roca, J., Quintián, H., & ROLLE, J. L. C. (2022) · Processes · doi:10.3390/pr10071406 · Cited by 10
Meggiorin, M., Bullo, P., Accoto, V., Passadore, G., Sottani, A., & Rinaldo, A. (2022) · Acque Sotterranee-Italian Journal of Groundwater · doi:10.7343/as-2022-573 · Cited by 7
“2022), and other statistical clustering methods often require the subjective definition of different parameters that influence the whole process (Aghabozorgi et al. 2015).”
BackgroundMethodSelf-reconstructive evidential clustering for high-dimensional data
Gong, C., Li, Y.-B., Fu, D., Liu, Y., Wang, P., & You, Y. (2022) · IEEE International Conference on Data Engineering · doi:10.1109/icde53745.2022.00203 · Cited by 7
Abideen, Z. U., Sun, H.-L., Yang, Z., & Fahim, H. (2021) · Expert Syst. J. Knowl. Eng. · doi:10.1111/exsy.12883 · Cited by 7
“(Aghabozorgi et al., 2015; Rani & Sikka, 2012) cluster the spatial – temporal features to employ the Poisson process and the Gaussian Mixture Model (GMM) (Kim et al., 2015).”
MethodSepulvado, B., Wood, M., Fridmanski, E. J., Wang, C., Chandler, M. J., Lizardo, O. A., & Hachen, D. S. (2020) · Social science computer review · doi:10.1177/0894439320923123 · Cited by 7
“…indices compare clusterings to previously labeled or ground truth data about which observations fall into which groups, while internal indices measure quality with recourse only to information about the set of clusters produced by an algorithm (Aghabozorgi et al., 2015; Arbelaitz et al., 2013).”
BackgroundMethodSemi-supervised time series classification method for quantum computing
Yarkoni, S., Kleshchonok, A., Dzerin, Y., Neukart, F., & Hilbert, M. (2021) · Quantum Machine Intelligence · doi:10.1007/s42484-021-00042-0 · Cited by 10
Genetic algorithm-based fuzzy clustering applied to multivariate time series
Ribeiro, K. D. P., Fontes, C., & de Melo, G. J. A. (2020) · Evolutionary Intelligence · doi:10.1007/s12065-020-00422-8 · Cited by 7
Large-Scale Time Series Clustering with k-ARs
Yue, Z., & Solo, V. (2020) · IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp40776.2020.9053016 · Cited by 7
“Further information can be found in the surveys [10, 11].”
BackgroundApproximate QoS Rule Derivation Based on Root Cause Analysis for Cloud Computing
Konno, S., & Défago, X. (2019) · Pacific Rim International Symposium on Dependable Computing · doi:10.1109/prdc47002.2019.00020 · Cited by 7
“For time-series analysis, clustering is a common solution performed to uncover these patterns on time-series datasets [4].”
BackgroundMethodInfluentialExperiments with Dissimilarity Measures for Clustering Waveform Data from Wearable Sensors
Datta, S., Bezdek, J., & Palaniswami, M. (2019) · IEEE Symposium Series on Computational Intelligence · doi:10.1109/ssci44817.2019.9003064 · Cited by 7
“…prices, weather, location trajectories, physiological vitals such as heart rate and blood pressure, biometrics, etc. Domains such as finance, government, healthcare and multimedia need algorithms that can analyze such waveform data, which has led to intensive research over the last decade [1], [2].”
BackgroundTopology-Based Clusterwise Regression for User Segmentation and Demand Forecasting
Rivera-Castro, R., Pletnev, A., Pilyugina, P., Diaz, G., Nazarov, I., Zhu, W., & Burnaev, E. (2019) · International Conference on Data Science and Advanced Analytics · doi:10.1109/dsaa.2019.00048 · Cited by 7
“In the context of time series, [24] argues that their unique characteristics make them unsuitable to conventional clustering algorithms.”
BackgroundZhou, S.-H., Xu, X., Xiao, Y.-Y., Chang, W., Qian, S., & Pan, X. (2019) · Entropy · doi:10.3390/e21090861 · Cited by 7
“…In matrix D , P ( P = q 1 , q 2 , … , q n In matrix D , P (P = q 1 , q 2 , . . . … , q N ) , q n , . . . denotes the dynamic time warping path of time series A and B , q i represents the distance of time series A and B at time point i. Path P , q N ) denotes the dynamic time warping path of time…”
BackgroundAn enhanced hybrid model for event prediction in healthcare time series
Mehrmolaei, S., & Keyvanpour, M. (2019) · Int. J. Knowl. Based Intell. Eng. Syst. · doi:10.3233/kes-190406 · Cited by 7
“In previously published resources, weighting process has been performed on multivariate time series, as a technique of feature selection for demonstrating the key role of some of the features in representation more accurate results when clustering task [21,22].”
A New Similarity Measure for Time Series Data Mining Based on Longest Common Subsequence
Soleimany, G., & Abessi, M. (2019) · American Journal of Data Mining and Knowledge Discovery · doi:10.11648/j.ajdmkd.20190401.16 · Cited by 7
“It should be noted that provide a suitable method for measuring the similarity of time series is one of the issues that has been widespread in time series data mining research in recent years [8].”
MethodInfluentialPantazis, D., Goodall, P., Conway, P., & West, A. (2019) · Mechanical systems and signal processing · doi:10.1016/j.ymssp.2019.01.023 · Cited by 7
Gloumakov, Y., Spiers, A., & Dollar, A. M. (2019) · IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2019.8794421 · Cited by 7
Mining Temporal Patterns to Discover Inter-Appliance Associations Using Smart Meter Data
Osama, S., Alfonse, M., & Salem, A. (2019) · Big Data and Cognitive Computing · doi:10.3390/bdcc3020020 · Cited by 7
“Agglomerative clustering is the opposite, where each object has its own cluster initially, and then clusters are merged when going up based on similarity or dissimilarity measures [6].”
Forecasting Drought Using Modified Empirical Wavelet Transform-ARIMA with Fuzzy C-Means Clustering
bin Shaari, M. A., Samsudin, R., & Ilman, A. B. S. (2018) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v11.i3.pp1152-1161 · Cited by 7
“Several studies have explored on the use of clustering analysis to further improve the forecast accuracy [17].”
BackgroundRegional Frequency Analysis of Precipitation Using Time Series Clustering Approaches
Zaifoglu, H., Akıntuğ, B., & Yanmaz, A. (2018) · Journal of hydrologic engineering · doi:10.1061/(asce)he.1943-5584.0001659 · Cited by 7
Simultaneous optimisation of clustering quality and approximation error for time series segmentation
Durán-Rosal, A. M., Gutiérrez, P. A., Martínez-Estudillo, F., & Hervás-Martínez, C. (2018) · Information Sciences · doi:10.1016/j.ins.2018.02.041 · Cited by 7
Predictive Maintenance in der industriellen Praxis
Bink, R., & Zschech, P. (2017) · HMD Praxis der Wirtschaftsinformatik · doi:10.1365/s40702-017-0378-2 · Cited by 7
“Die TFDF wurden demgegenüber mithilfe einer Short Time Fast Fourier Transformation (STFT) extrahiert, die auch auf nicht stationären Signalen angewandt werden können (Aghabozorgi et al. 2015).”
MethodAkimov, A., & Sirota, A. A. (2017) · Radioelectronics and Communications Systems · doi:10.3103/s0735272717100041 · Cited by 7
“In particular, the effect of random character DD in recognizing digital video signals results in the nonlinear distortion of their waveform with local compression or spreading in reference to the time axis [1].”
Background
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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