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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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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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“Large, high-dimensional spatiotemporal data cannot be managed by existing data reduction algorithms within a tolerable time frame and acceptable quality (Aghabozorgi, Seyed Shirkhorshidi, and Ying Wah 2015; García, Luengo, and Herrera 2015).”
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“Developments on real-time applications can be also found in the literature [103].”
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“Methods for time series clustering exist in other research areas such as statistics, finance, medicine, and economics, but not all of these new methods have made it to astronomy (for a review of time series clustering methods, see Aghabozorgi et al. 2015).”
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“More precisely, the user provides (i) a time series T , (ii) a choice between fixed-length segmentation algorithm [14, 20] and variable-length/window-based segmentation algorithm [34, 28, 29, 5, 22, 39] , and (iii) the identifier of a compression function family (e.g., Polynomial function family).”
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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