CoChat
Sign Up
Cited by 1,826Time Series Analysis and ForecastingAdvanced Clustering Algorithms ResearchData Management and Algorithms

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Time-series clustering – A decade review

Aghabozorgi, Saeed, Seyed Shirkhorshidi, Ali, Ying Wah, Teh·2015·Information Systems
DOI 10.1016/j.is.2015.04.007
Open this paper in CoChat — free account

Overview

Integrity signals

Registry

Crossref · 10.1016/j.is.2015.04.007

Cites

0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Citations

Cites

Flagged first, then by citations

Show all 4 references

Research works printed but not linked: 225

  1. Rai, 2010, A survey of clustering techniques, Int. J. Comput. Appl., 7, 1
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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.
  8. Keogh, 2004, Segmenting time series: a survey and novel approach, Data Min. Time Ser. Databases, 57, 1, 10.1142/9789812565402_0001 · DOI
  9. 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
  10. 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
  11. 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
  12. 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.
  13. Laxman, 2006, A survey of temporal data mining, Sadhana, 31, 173, 10.1007/BF02719780 · DOI
  14. Kavitha, 2010, Clustering time series data stream—a literature survey, Int. J. Comput. Sci. Inf. Secur., 8
  15. C. Antunes, A.L. Oliveira, Temporal data mining: an overview, in: KDD Workshop on Temporal Data Mining, 2001, pp. 1–13.
  16. Warrenliao, 2005, Clustering of time series data—a survey, Pattern Recognit., 38, 1857, 10.1016/j.patcog.2005.01.025 · DOI
  17. Rani, 2012, Recent techniques of clustering of time series data: a survey, Int. J. Comput. Appl, 52, 1
  18. Lin, 2004, Iterative incremental clustering of time series, Adv. Database Technol, 2004, 521
  19. 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.
  20. Faloutsos, 1994, Fast subsequence matching in time-series databases, ACM SIGMOD Rec., 23, 419, 10.1145/191843.191925 · DOI
  21. Wang, 2006, Characteristic-based clustering for time series data, Data Min. Knowl. Discov., 13, 335, 10.1007/s10618-005-0039-x · DOI
  22. Chiş, 2009, Clustering time series data: an evolutionary approach, Found. Comput. Intell., 6, 193
  23. 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
  24. Keogh, 2000, A simple dimensionality reduction technique for fast similarity search in large time series databases, Knowl. Inf. Syst., 1805, 122
  25. Zhang, 2006, Unsupervised feature extraction for time series clustering using orthogonal wavelet transform, Informatica, 30, 305
  26. 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
  27. Das, 1998, Rule discovery from time series,, Knowl. Discov. Data Min, 98, 16
  28. 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.
  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
  30. 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
  31. 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
  32. 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.
  33. 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
  34. He, 2011, A new method for abrupt dynamic change detection of correlated time series,, Int. J. Climatol., 32, 1604, 10.1002/joc.2367 · DOI
  35. 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
  36. Pavlidis, 2006, Financial forecasting through unsupervised clustering and neural networks, Oper. Res., 6, 103
  37. F. Ito, T. Hiroyasu, M. Miki, H. Yokouchi, Detection of Preference Shift Timing using Time-Series Clustering, 2009, pp. 1585–1590. · DOI
  38. 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
  39. Rebbapragada, 2009, Finding anomalous periodic time series, Mach. Learn., 74, 281, 10.1007/s10994-008-5093-3 · DOI
  40. Subhani, 2010, Multiple gene expression profile alignment for microarray time-series data clustering, Bioinformatics, 26, 2281, 10.1093/bioinformatics/btq422 · DOI
  41. 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
  42. Möller-Levet, 2003, Fuzzy clustering of short time-series and unevenly distributed sampling points, Adv. Intell. Data Anal., 330
  43. Ernst, 2005, Clustering short time series gene expression data, Bioinforma., 21, i159, 10.1093/bioinformatics/bti1022 · DOI
  44. Pyatnitskiy, 2014, Clustering gene expression regulators: new approach to disease subtyping, PLoS One, 9, e84955, 10.1371/journal.pone.0084955 · DOI
  45. 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
  46. Ji, 2013, A dynamic fuzzy cluster algorithm for time series, Abstr. Appl. Anal., 2013, 1
  47. 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
  48. Košmelj, 1990, Cross-sectional approach for clustering time varying data, J. Classif, 7, 10.1007/BF01889706 · DOI
  49. Iglesias, 2013, Analysis of similarity measures in times series clustering for the discovery of building energy patterns, Energies, 6, 579, 10.3390/en6020579 · DOI
  50. 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
  51. Shumway, 2003, Time-frequency clustering and discriminant analysis, Stat. Probab. Lett, 63, 307, 10.1016/S0167-7152(03)00095-6 · DOI
  52. 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
  53. 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
  54. 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
  55. M. Kumar, N.R. Patel, Clustering seasonality patterns in the presence of errors, in: Proceedings of Eighth ACM SIGKDD, 2002, pp. 557–563. · DOI
  56. 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
  57. 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
  58. 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
  59. 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
  60. 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
  61. 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
  62. Wismüller, 2002, Cluster analysis of biomedical image time-series, Int. J. Comput. Vis, 46, 103, 10.1023/A:1013550313321 · DOI
  63. van den Heuvel, 2008, Normalized cut group clustering of resting-state fMRI data, PLoS One, 3, e2001, 10.1371/journal.pone.0002001 · DOI
  64. Gullo, 2011, A time series approach for clustering mass spectrometry data, J. Comput. Sci., 3, 344, 10.1016/j.jocs.2011.06.008 · DOI
  65. 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
  66. M. Ramoni, P. Sebastiani, P. Cohen, Multivariate clustering by dynamics, in :Proceedings of the national Conference on Artificial Intelligence, 2000, pp. 633–638.
  67. 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
  68. Tran, 2002, Fuzzy c-means clustering-based speaker verification, Adv. Soft Comput. 2002, 2275, 363
  69. Fong, 2012, Using hierarchical time series clustering algorithm and wavelet classifier for biometric voice classification, J. Biomed. Biotechnol., 215019
  70. 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
  71. 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
  72. 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
  73. Morchen, 2005, Extracting interpretable muscle activation patterns with time series knowledge mining, J. Knowl. BASED, 9, 197
  74. 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
  75. Mitsa, 2009, vol. 33
  76. 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
  77. 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
  78. 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
  79. 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
  80. 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
  81. 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.
  82. Keogh, 2001, Locally adaptive dimensionality reduction for indexing large time series databases,, ACM SIGMOD Rec, 27, 151, 10.1145/376284.375680 · DOI
  83. 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
  84. 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
  85. 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.
  86. 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
  87. 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
  88. 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
  89. Wang, 2012, Experimental comparison of representation methods and distance measures for time series data, Data Min. Knowl. Discov.
  90. 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.
  91. 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
  92. Korn, 1997, Efficiently supporting ad hoc queries in large datasets of time sequences, ACM SIGMOD Record, 26, 289, 10.1145/253262.253332 · DOI
  93. Portet, 2009, Automatic generation of textual summaries from neonatal intensive care data, Artif. Intell., 173, 789, 10.1016/j.artint.2008.12.002 · DOI
  94. Y. Cai and R. Ng, Indexing spatio-temporal trajectories with Chebyshev polynomials, in: Procedings of 2004 ACM SIGMOD International, 2004, p. 599. · DOI
  95. 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
  96. 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.
  97. 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
  98. Minnen, 2007, Discovering multivariate motifs using subsequence density estimation and greedy mixture learning, Proc. Natl. Conf. Artif. Intell., 22, 615
  99. 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
  100. 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
  101. 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
  102. 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
  103. Agrawal, 1993, Efficient similarity search in sequence databases, Found. Data Organ. Algorithms, 46, 69, 10.1007/3-540-57301-1_5 · DOI
  104. 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
  105. 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.
  106. 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
  107. Bagnall, 2005, Clustering time series with clipped data, Mach. Learn., 58, 151, 10.1007/s10994-005-5825-6 · DOI
  108. 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
  109. 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
  110. 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
  111. Smyth, 1997, Clustering sequences with hidden Markov models,, Adv. Neural Inf. Process. Syst, 9, 648
  112. Xiong, 2002, Mixtures of ARMA models for model-based time series clustering, Data Min, 2002. ICDM, 2003, 717
  113. Sakoe, 1971, A dynamic programming approach to continuous speech recognition,, Proceedings of the Seventh International Congress on Acoustics, vol. 3, 65
  114. M. Vlachos, G. Kollios, D. Gunopulos, Discovering similar multidimensional trajectories, in: Proceedingsof 18th International Conference on Data Engineering, 2002, pp. 673–684. · DOI
  115. 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.
  116. Latecki, 2005, Elastic partial matching of time series,, Knowl. Discov. Databases PKDD 2005, 577, 10.1007/11564126_60 · DOI
  117. Keogh, 2007, Compression-based data mining of sequential data,, Data Min. Knowl. Discov., 14, 99, 10.1007/s10618-006-0049-3 · DOI
  118. Rodgers, 1988, Thirteen ways to look at the correlation coefficient,, Am. Stat, 42, 59, 10.2307/2685263 · DOI
  119. 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.
  120. 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
  121. Last, 2004, Data mining in time series databases, World Sci.
  122. 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
  123. Aach, 2001, Aligning gene expression time series with time warping algorithms, Bioinformatics, 17, 495, 10.1093/bioinformatics/17.6.495 · DOI
  124. 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
  125. 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.
  126. Golay, 1998, A new correlation-based fuzzy logic clustering algorithm for FMRI,, Magn. Reson. Med., 40, 249, 10.1002/mrm.1910400211 · DOI
  127. Wang, 2000, Supporting content-based searches on time series via approximation,, Sci Stat Database, 69, 10.1109/SSDM.2000.869779 · DOI
  128. 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
  129. 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
  130. Chen, 2005, Using multi-scale histograms to answer pattern existence and shape match queries,, Time, 2, 217
  131. Aßfalg, 2006, Similarity search on time series based on threshold queries, Adv. Database Technol. 2006, 276, 10.1007/11687238_19 · DOI
  132. 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
  133. 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
  134. 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
  135. Zhang, 2009, A notime series classificationvel pattern extraction method for, Optim. Eng., 10, 253, 10.1007/s11081-008-9056-0 · DOI
  136. Lang, 2010, Dictionary-based compression for long time-series similarity, Knowl. Data Eng. IEEE Trans, 22, 1609, 10.1109/TKDE.2009.201 · DOI
  137. Salvador, 2007, Toward accurate dynamic time warping in linear time and space, Intell. Data Anal., 11, 561, 10.3233/IDA-2007-11508 · DOI
  138. 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
  139. 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
  140. 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
  141. 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
  142. 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
  143. Kaufman, 1990, vol. 39
  144. Vuori, 2002, A comparison of techniques for automatic clustering of handwritten characters, Pattern Recognit., 2002, 3, 30168
  145. 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.
  146. 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
  147. Gupta, 1996, Nonlinear alignment and averaging for estimating the evoked potential, IEEE Trans. Biomed. Eng., 43, 348, 10.1109/10.486255 · DOI
  148. 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
  149. Oates, 2001, Using dynamic time warping to bootstrap HMM-based clustering of time series, Seq. Learn. Paradig. ALGORITHMS, Appl, 1, 35
  150. 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
  151. 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
  152. 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
  153. 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.
  154. Aghabozorgi, 2012, Incremental clustering of time-series by fuzzy clustering, J. Inf. Sci. Eng., 28, 671
  155. Karypis, 1999, Chameleon: hierarchical clustering using dynamic modeling, Comput. (Long. Beach. Calif)., 32, 68
  156. Guha, 1998, CURE: an efficient clustering algorithm for large databases,, ACM SIGMOD Rec., 27, 73, 10.1145/276305.276312 · DOI
  157. Vlachos, 2003, A wavelet-based anytime algorithm for k-means clustering of time series, Proc. Work. Clust, 23
  158. 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
  159. 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.
  160. Hirano, 2005, Empirical comparison of clustering methods for long time-series databases, Act. Min, 3430, 268, 10.1007/11423270_15 · DOI
  161. 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.
  162. 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.
  163. 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.
  164. Bradley, 1998, Scaling clustering algorithms to large databases, Knowl. Discov. Data Min, 9
  165. Beringer, 2006, Online clustering of parallel data streams, Data Knowl. Eng., 58, 180, 10.1016/j.datak.2005.05.009 · DOI
  166. Dunn, 1973, A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, Cybern. Syst., 3, 32
  167. Krishnapuram, 2001, “Low-complexity fuzzy relational clustering algorithms for web mining,”, Fuzzy Syst. IEEE Trans, vol. 9, 595, 10.1109/91.940971 · DOI
  168. Dembélé, 2003, Fuzzy C-means method for clustering microarray data, Bioinformatics, 19, 973, 10.1093/bioinformatics/btg119 · DOI
  169. 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
  170. Fisher, 1987, Knowledge acquisition via incremental conceptual clustering, Mach. Learn., 2, 139, 10.1007/BF00114265 · DOI
  171. Kohonen, 1990, The self-organizing map, Proc. IEEE, 78, 1464, 10.1109/5.58325 · DOI · Paper page
  172. 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
  173. Bicego, 2003, Similarity-based clustering of sequences using hidden Markov models, Mach. Learn. data Min. pattern Recognit, 2734, 95
  174. 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
  175. Andreopoulos, 2009, , A roadmap of clustering algorithms: finding a match for a biomedical application, Brief. Bioinform., 10, 297, 10.1093/bib/bbn058 · DOI
  176. Ester, 1996, A density-based algorithm for discovering clusters in large spatial databases with noise, In Kdd, 96, 226
  177. Ankerst, 1999, OPTICS: Ordering points to identify the clustering structure, ACM SIGMOD Rec, 28, 40, 10.1145/304181.304187 · DOI
  178. 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
  179. 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.
  180. 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.
  181. Kakizawa, 1998, Discrimination and clustering for multivariate time series, J. Am. Stat. Assoc, 93, 328, 10.1080/01621459.1998.10474114 · DOI
  182. Policker, 2000, Nonstationary time series analysis by temporal clustering, Syst. Man, Cybern. Part B, 30, 339, 10.1109/3477.836381 · DOI
  183. 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
  184. 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
  185. Wang, 2005, Dimension reduction for clustering time series using global characteristics, Comput. Sci. 2005, 792, 10.1007/11428862_108 · DOI
  186. 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.
  187. 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
  188. Bao, 2007, A generalized model for financial time series representation and prediction,, Appl. Intell., 29, 1, 10.1007/s10489-007-0063-1 · DOI
  189. 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
  190. 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
  191. 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.
  192. 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
  193. Zhang, 2011, A novel clustering method on time series data, Expert Syst. Appl., 38, 11891, 10.1016/j.eswa.2011.03.081 · DOI
  194. 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
  195. Darkins, 2013, Accelerating Bayesian hierarchical clustering of time series data with a randomised algorithm, PLoS One, 8, e59795, 10.1371/journal.pone.0059795 · DOI
  196. 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
  197. Ghassempour, 2014, Clustering multivariate time series using hidden Markov models, Int. J. Environ. Res. Public Health, 11, 2741, 10.3390/ijerph110302741 · DOI
  198. 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
  199. 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.
  200. 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

200 of 225 shown

Cited by

Sorted by citations

Items 901–1,000 of 1,000

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)
DOI
10.1016/j.is.2015.04.007
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
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review