Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 2,401

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Clustering of time series data—a survey

Warren Liao, T.·2005·Pattern Recognition
DOI 10.1016/j.patcog.2005.01.025
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.patcog.2005.01.025

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 5 references

Research works printed but not linked: 54

  1. J. MacQueen, Some methods for classification and analysis of multivariate observations, in: L.M. LeCam, J. Neyman (Eds.), Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297.
  2. Krishnapuram, 2001, Low-complexity fuzzy relational clustering algorithms for web mining, IEEE Trans. Fuzzy Systems, 9, 595, 10.1109/91.940971 · DOI · Paper page
  3. Krishnapuram, 1999, A note on the Gustafson–Kessel and adaptive fuzzy clustering algorithms, IEEE Trans. Fuzzy Systems, 7, 453, 10.1109/91.784208 · doi:10.1109/91.784208
  4. Krishna, 1999, Genetic k-means algorithms, IEEE Trans. Syst. Man Cybernet.-B, 29, 433, 10.1109/3477.764879 · DOI · Paper page
  5. Meng, 2002, A genetic hard c-means clustering algorithm, Dyn. Continuous Discrete Impulsive Syst. Ser. B: Appl. Algorithms, 9, 421
  6. Hall, 1999, Clustering with a genetically optimized approach, IEEE Trans. Evolutionary Computat., 3, 103, 10.1109/4235.771164 · DOI · Paper page
  7. Karypis, 1999, Chameleon: hierarchical clustering using dynamic modeling, Computer August, 68, 10.1109/2.781637 · DOI · Paper page
  8. S. Guha, R. Rastogi, K. Shim, CURE: an efficient clustering algorithm for large databases, Proceedings of the 1998 ACM-SIGMOD International Conference on Management of Data, Seattle, WA, June 1998, pp. 73–84. · DOI · Paper page
  9. T. Zhang, R. Ramakrishnan, M. Livny, BIRCH: an efficient data clustering method for very large databases, Proceedings of the 1996 ACM-SIGMOD International Conference on Management of Data, Montreal, Canada, June 1996, pp. 103–114. · DOI · Paper page
  10. M. Ester, H.-P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases, Proceedings of the 1996 International Conference on Knowledge Discovery and Data Mining (KDD’96), Portland, OR, 1996, pp. 226–231.
  11. W. Wang, J. Yang, R. Muntz, R., STING: a statistical information grid approach to spatial data mining, Proceedings of the 1997 International Conference on Very Large Data Base (VLDB’97), Athens, Greek, 1997, pp. 186–195.
  12. Kohonen, 1990, The self organizing maps, Proc. IEEE, 78, 1464, 10.1109/5.58325 · DOI · Paper page
  13. Dunn, 1974, A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, J. Cybernet., 3, 32, 10.1080/01969727308546046 · DOI · Paper page
  14. Golay, 1998, A new correlation-based fuzzy logic clustering algorithm for fMRI, Mag. Resonance Med., 40, 249, 10.1002/mrm.1910400211 · DOI · Paper page
  15. C.S. Möller-Levet, F. Klawonn, K.-H. Cho, O. Wolkenhauer, Fuzzy clustering of short time series and unevenly distributed sampling points, Proceedings of the 5th International Symposium on Intelligent Data Analysis, Berlin, Germany, August 28–30, 2003. · DOI
  16. M. Kumar, N.R. Patel, J. Woo, Clustering seasonality patterns in the presence of errors, Proceedings of KDD ’02, Edmonton, Alberta, Canada. · DOI
  17. Kakizawa, 1998, Discrimination and clustering for multivariate time series, J. Amer. Stat. Assoc., 93, 328, 10.2307/2669629 · doi:10.2307/2669629
  18. Dahlhaus, 1996, On the Kullback–Leibler information divergence of locally stationary processes, Stochastic Process. Appl., 62, 139, 10.1016/0304-4149(95)00090-9 · DOI
  19. Shumway, 2003, Time–frequency clustering and discriminant analysis, Stat. Probab. Lett., 63, 307, 10.1016/S0167-7152(03)00095-6 · DOI
  20. Bezdek, 1998, Some new indexes of cluster validity, IEEE Trans. Syst. Man Cybernet. B: Cybernet., 28, 301, 10.1109/3477.678624 · DOI · Paper page
  21. Košmelj, 1990, Cross-sectional approach for clustering time varying data, J. Classification, 7, 99, 10.1007/BF01889706 · DOI
  22. Baragona, 2001, A simulation study on clustering time series with meta-heuristic methods, Quad. Stat., 3, 1
  23. Schwartz, 1978, Estimating the dimension of a model, Ann. Stat., 6, 461, 10.1214/aos/1176344136 · DOI · Paper page
  24. 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 · Paper page
  25. T.W. Liao, B. Bolt, J. Forester, E. Hailman, C. Hansen, R.C. Kaste, J. O’May, Understanding and projecting the battle state, 23rd Army Science Conference, Orlando, FL, December 2–5, 2002.
  26. J.J. van Wijk, E.R. van Selow, Cluster and calendar based visualization of time series data, Proceedings of IEEE Symposium on Information Visualization, San Francisco, CA, October 25–26, 1999. · DOI
  27. Wismüller, 2002, Cluster analysis of biomedical image time series, Int. J. Comput. Vision, 46, 103, 10.1023/A:1013550313321 · DOI
  28. Policker, 2000, Nonstationary time series analysis by temporal clustering, IEEE Trans. Syst. Man Cybernet.-B: Cybernet., 30, 339, 10.1109/3477.836381 · DOI
  29. Gath, 1989, Unsupervised optimal fuzzy clustering, IEEE Trans. Pattern Anal. Mach. Intell., 7, 773, 10.1109/34.192473 · DOI · Paper page
  30. Wilpon, 1985, Modified k-means clustering algorithm for use in isolated word recognition, IEEE Trans. Acoust. Speech Signal Process., 33, 587, 10.1109/TASSP.1985.1164581 · DOI · Paper page
  31. Shaw, 1992, Using cluster analysis to classify time series, Physica D, 58, 288, 10.1016/0167-2789(92)90117-6 · doi:10.1016/0167-2789(92)90117-6
  32. Goutte, 1999, On clustering fMRI time series, Neuroimage, 9, 298, 10.1006/nimg.1998.0391 · DOI · Paper page
  33. Goutte, 2001, Feature-space clustering for fMRI meta-analysis, Hum. Brain Mapping, 13, 165, 10.1002/hbm.1031 · DOI
  34. T.-C. Fu, F.-L. Chung, V. Ng, R. Luk, Pattern discovery from stock time series using self-organizing maps, KDD 2001 Workshop on Temporal Data Mining, August 26–29, San Francisco, 2001, pp. 27–37.
  35. Owsley, 1997, Self-organizing feature maps and hidden Markov models for machine-tool monitoring, IEEE Trans. Signal Process., 45, 2787, 10.1109/78.650105 · DOI
  36. M. Vlachos, J. Lin, E. Keogh, D. Gunopulos, A wavelet-based anytime algorithm for k-means clustering of time series, Proceedings of the Third SIAM International Conference on Data Mining, San Francisco, CA, May 1–3, 2003.
  37. Piccolo, 1990, A distance measure for classifying ARMA models, J. Time Ser. Anal., 11, 153, 10.1111/j.1467-9892.1990.tb00048.x · doi:10.1111/j.1467-9892.1990.tb00048.x
  38. Beran, 1999, Visualizing the relationship between time series by hierarchical smoothing models, J. Comput. Graph. Stat., 8, 213, 10.2307/1390634 · doi:10.2307/1390634
  39. Maharaj, 2000, Clusters of time series, J. Classification, 17, 297, 10.1007/s003570000023 · DOI · Paper page
  40. Ramoni, 2002, Bayesian clustering by dynamics, Mach. Learning, 47, 91, 10.1023/A:1013635829250 · DOI · Paper page
  41. M. Ramoni, P. Sebastiani, P. Cohen, Multivariate clustering by dynamics, Proceedings of the 2000 National Conference on Artificial Intelligence (AAAI-2000), San Francisco, CA, 2000, pp. 633–638.
  42. K. Kalpakis, D. Gada, V. Puttagunta, Distance measures for effective clustering of ARIMA time-series, Proceedings of the 2001 IEEE International Conference on Data Mining, San Jose, CA, November 29–December 2, 2001, pp. 273–280. · DOI · Paper page
  43. Y. Xiong, D.-Y. Yeung, Mixtures of ARMA models for model-based time series clustering, Proceedings of the IEEE International Conference on Data Mining, Maebaghi City, Japan, 9–12 December, 2002.
  44. D. Tran, M. Wagner, Fuzzy c-means clustering-based speaker verification, in: N.R. Pal, M. Sugeno (Eds.), AFSS 2002, Lecture Notes in Artificial Intelligence, 2275, 2002, pp. 318–324. · DOI
  45. T. Oates, L. Firoiu, P.R. Cohen, Clustering time series with hidden Markov models and dynamic time warping, Proceedings of the IJCAI-99 Workshop on Neural, Symbolic, and Reinforcement Learning Methods for Sequence Learning.
  46. C. Li, G. Biswas, Temporal pattern generation using hidden Markov model based unsupervised classification, in: D.J. Hand, J.N. Kok, M.R. Berthold (Eds.), Lecture Notes in Computer Science, vol. 164, IDA ’99, Springer, Berlin, 1999, pp. 245–256. · DOI
  47. C. Li, G. Biswas, M. Dale, P. Dale, Building models of ecological dynamics using HMM based temporal data clustering—a preliminary study, in: F. Hoffmann et al. (Eds.), IDA 2001, Lecture Notes in Computer Science, vol. 2189, 2001, pp. 53–62. · DOI
  48. Wang, 2002, Hidden Markov model-based wear monitoring in turning, J. Manufacturing Sci. Eng., 124, 651, 10.1115/1.1475320 · DOI · Paper page
  49. Josien, 2002, Simultaneous grouping of parts and machines with an integrated fuzzy clustering method, Fuzzy Sets Syst., 126, 1, 10.1016/S0165-0114(01)00063-X · doi:10.1016/s0165-0114(01)00063-x
  50. Ho, 1994, Decision combination in multiple classifier systems, IEEE Trans. Pattern Anal. Mach. Intell., 16, 66, 10.1109/34.273716 · DOI · Paper page
  51. Ng, 1998, Democracy in pattern classifications: combinations of votes from various pattern classifiers, Artif. Intell. Eng., 12, 189, 10.1016/S0954-1810(97)00016-2 · doi:10.1016/s0954-1810(97)00016-2
  52. R. Ng, J. Han, Efficient and effective clustering method for spatial data mining, Proceedings of the 1994 International Conference on Very Large Data Bases (VLDB’94), Santiago, Chile, September 1994, pp. 144–155.
  53. Ananthanarayana, 2001, Efficient clustering of large data sets, Pattern Recognition, 34, 2561, 10.1016/S0031-3203(01)00097-8 · DOI
  54. Roddick, 2002, A survey of temporal knowledge discovery paradigms and methods, IEEE Trans. Knowledge Data Eng., 14, 750, 10.1109/TKDE.2002.1019212 · DOI · Paper page

Cited by

See all 2,401

Metadata

Authors
Warren Liao, T.
Venue
Pattern Recognition
Publisher
Elsevier BV
Published
2005-11
Volume
38
Issue
11
Pages
1857-1874
ISSN
0031-3203 (ISSN-L)
DOI
10.1016/j.patcog.2005.01.025
OpenAlex
W2097747115
Cited by
2,401
References
5
FWCI
34.63
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)

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

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Clustering of time series data—a survey. https://app.cochat.ai/papers/10.1016/j.patcog.2005.01.025
The paper
Warren Liao, T. (2005). Clustering of time series data—a survey. Pattern Recognition. https://doi.org/10.1016/j.patcog.2005.01.025

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.