A dynamic fuzzy cluster algorithm for time series. (English) Zbl 1272.68391

Summary: This paper presents an efficient algorithm, called dynamic fuzzy cluster (DFC), for dynamically clustering time series by introducing the definition of key point and improving the FCM algorithm. The proposed algorithm works by determining those time series whose class labels are vague and further partitions them into different clusters over time. The main advantage of this approach compared with other existing algorithms is that the property of some time series belonging to different clusters over time can be partially revealed. Results from simulation-based experiments on geographical data demonstrate the excellent performance and the desired results have been obtained. The proposed algorithm can be applied to solve other clustering problems in data mining.


68T37 Reasoning under uncertainty in the context of artificial intelligence
68T10 Pattern recognition, speech recognition
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)
Full Text: DOI


[1] D’Urso, P.; Maharaj, E. A., Autocorrelation-based fuzzy clustering of time series, Fuzzy Sets and Systems, 160, 24, 3565-3589 (2009)
[2] D’Urso, P.; Maharaj, E. A., Wavelets-based clustering of multivariate time series, Fuzzy Sets and Systems, 193, 33-61 (2012) · Zbl 1237.62079
[3] Liao, T. W., Clustering of time series data—a survey, Pattern Recognition, 38, 11, 1857-1874 (2005) · Zbl 1077.68803
[4] Chakrabarti, D.; Kumar, R.; Tomkins, A., Evolutionary Clustering, Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’06)
[5] Chi, Y.; Song, X. D.; Zhou, D. Y.; Hino, K.; Tseng, B. L., On evolutionary spectral clustering, ACM Transactions on Knowledge Discovery from Data, 3, 4, article 17 (2009)
[6] Corduas, M.; Piccolo, D., Time series clustering and classification by the autoregressive metric, Computational Statistics & Data Analysis, 52, 4, 1860-1872 (2008) · Zbl 1452.62624
[7] Xiong, Y. M.; Yeung, D. Y., Time series clustering with ARMA mixtures, Pattern Recognition, 37, 8, 1675-1689 (2004) · Zbl 1117.62488
[8] Vilar, J. A.; Alonso, A. M.; Vilar, J. M., Non-linear time series clustering based on non-parametric forecast densities, Computational Statistics & Data Analysis, 54, 11, 2850-2865 (2010) · Zbl 1284.62575
[9] Brida, J. G.; Gómez, D. M.; Risso, W. A., Symbolic hierarchical analysis in currency markets: an application to contagion in currency crises, Expert Systems with Applications, 36, 4, 7721-7728 (2009)
[10] Zhang, X. H.; Liu, J. Q.; Du, Y.; Lv, T. J., A novel clustering method on time series data, Expert Systems with Applications, 38, 9, 11891-11900 (2011)
[11] Chiang, M. C.; Tsai, C. W.; Yang, C. S., A time-efficient pattern reduction algorithm for k-means clustering, Information Sciences, 181, 4, 716-731 (2011)
[12] Keogh, E.; Kasetty, S., On the need for time series data mining benchmarks: a survey and empirical demonstration, Data Mining and Knowledge Discovery, 7, 4, 349-371 (2003)
[13] Keogh, E.; Lin, J.; Truppel, W., Clustering of time series subsequences is meaningless: implications for previous and future research, Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM ’03)
[14] Rakthanmanon, T.; Keogh, E.; Lonardi, S.; Evans, S., Time series epenthesis: clustering time series streams requires ignoring some data, Proceedings of the IEEE 11th International Conference on Data Mining (ICDM ’11)
[15] Keogh, E.; Xi, X.; Wei, L.; Ratanamahatana, C. A.
[16] Fu, T. C., A review on time series data mining, Engineering Applications of Artificial Intelligence, 24, 1, 164-181 (2011)
[17] Jain, A. K., Data clustering: 50 years beyond K-means, Pattern Recognition Letters, 31, 8, 651-666 (2010)
[18] Dunn, J. C., A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, Journal of Cybernetics, 3, 3, 32-57 (1973) · Zbl 0291.68033
[19] Höppner, F.; Klawonn, F.; Adams, N. M.; Robardet, C.; Siebes, A.; Boulicaut, J. F., Compensation of translational displacement in time series clustering using cross correlation, Advances in Intelligent Data Analysis VIII, 71-82 (2009), Berlin, Germany: Springer, Berlin, Germany
[20] Klawonn, F., Fuzzy clustering: insights and a new approach, Mathware & Soft Computing, 11, 2-3, 125-142 (2004) · Zbl 1105.68414
[21] Killick, R.; Eckley, I. A.; Ewans, K.; Jonathan, P., Detection of changes in variance of oceanographic time-series using changepoint analysis, Ocean Engineering, 37, 13, 1120-1126 (2010)
[22] Eschrich, S.; Ke, J. W.; Hall, L. O.; Goldgof, D. B., Fast accurate fuzzy clustering through data reduction, IEEE Transactions on Fuzzy Systems, 11, 2, 262-270 (2003)
[23] Möller-Levet, C. S.; Klawonn, F.; Cho, K. H.; Wolkenhauer, O., Fuzzy clustering of short time-series and unevenly distributed sampling points, Advances in Intelligent Data Analysis V. Advances in Intelligent Data Analysis V, Lecture Notes in Computer Science, 2810, 330-340 (2003)
[24] Nasibov, E. N.; Peker, S., Time series labeling algorithms based on the K-nearest neighbors’ frequencies, Expert Systems with Applications, 38, 5, 5028-5035 (2011)
[25] Kannan, S. R.; Ramathilagam, S.; Chung, P. C., Effective fuzzy c-means clustering algorithms for data clustering problems, Expert Systems With Applications, 39, 6292-6300 (2012)
[26] Mennis, J.; Guo, D. S., Spatial data mining and geographic knowledge discovery—an introduction, Computers, Environment and Urban Systems, 33, 6, 403-408 (2009)
[27] Macchiato, M. F.; la Rotonda, L.; Lapenna, V.; Ragosta, M., Time modelling and spatial clustering of daily ambient temperature: an application in southern Italy, Environmetrics, 6, 1, 31-53 (1995)
[28] Cowpertwait, P. S. P.; Cox, T. F., Clustering population means under heterogeneity of variance with an application to a rainfall time series problem, The Statistician, 41, 1, 113-121 (1992)
[29] Horenko, I., On clustering of non-stationary meteorological time series, Dynamics of Atmospheres and Oceans, 49, 2-3, 164-187 (2010)
[30] Wang, N. Y.; Chen, S. M., Temperature prediction and TAIFEX forecasting based on automatic clustering techniques and two-factors high-order fuzzy time series, Expert Systems with Applications, 36, 2, 2143-2154 (2009)
[31] Alonso, A. M.; Berrendero, J. R.; Hernández, A.; Justel, A., Time series clustering based on forecast densities, Computational Statistics & Data Analysis, 51, 2, 762-776 (2006) · Zbl 1157.62484
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.