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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 183410, 7 pages
A Dynamic Fuzzy Cluster Algorithm for Time Series
1School of Computer Science, Liaoning Normal University, Dalian, Liaoning 116081, China
2School of Urban and Environmental Science, Liaoning Normal University, Dalian, Liaoning 116029, China
3The School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
Received 19 December 2012; Accepted 25 March 2013
Academic Editor: Jianhong (Cecilia) Xia
Copyright © 2013 Min Ji et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- P. D'Urso and E. A. Maharaj, “Autocorrelation-based fuzzy clustering of time series,” Fuzzy Sets and Systems, vol. 160, no. 24, pp. 3565–3589, 2009.
- P. D'Urso and E. A. Maharaj, “Wavelets-based clustering of multivariate time series,” Fuzzy Sets and Systems, vol. 193, pp. 33–61, 2012.
- T. W. Liao, “Clustering of time series data—a survey,” Pattern Recognition, vol. 38, no. 11, pp. 1857–1874, 2005.
- D. Chakrabarti, R. Kumar, and A. Tomkins, “Evolutionary Clustering,” in Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '06), pp. 554–560, Philadelphia, Pa, USA, 2006.
- Y. Chi, X. D. Song, D. Y. Zhou, K. Hino, and B. L. Tseng, “On evolutionary spectral clustering,” ACM Transactions on Knowledge Discovery from Data, vol. 3, no. 4, article 17, 2009.
- M. Corduas and D. Piccolo, “Time series clustering and classification by the autoregressive metric,” Computational Statistics & Data Analysis, vol. 52, no. 4, pp. 1860–1872, 2008.
- Y. M. Xiong and D. Y. Yeung, “Time series clustering with ARMA mixtures,” Pattern Recognition, vol. 37, no. 8, pp. 1675–1689, 2004.
- J. A. Vilar, A. M. Alonso, and J. M. Vilar, “Non-linear time series clustering based on non-parametric forecast densities,” Computational Statistics & Data Analysis, vol. 54, no. 11, pp. 2850–2865, 2010.
- J. G. Brida, D. M. Gómez, and W. A. Risso, “Symbolic hierarchical analysis in currency markets: an application to contagion in currency crises,” Expert Systems with Applications, vol. 36, no. 4, pp. 7721–7728, 2009.
- X. H. Zhang, J. Q. Liu, Y. Du, and T. J. Lv, “A novel clustering method on time series data,” Expert Systems with Applications, vol. 38, no. 9, pp. 11891–11900, 2011.
- M. C. Chiang, C. W. Tsai, and C. S. Yang, “A time-efficient pattern reduction algorithm for k-means clustering,” Information Sciences, vol. 181, no. 4, pp. 716–731, 2011.
- E. Keogh and S. Kasetty, “On the need for time series data mining benchmarks: a survey and empirical demonstration,” Data Mining and Knowledge Discovery, vol. 7, no. 4, pp. 349–371, 2003.
- E. Keogh, J. Lin, and W. Truppel, “Clustering of time series subsequences is meaningless: implications for previous and future research,” in Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM '03), pp. 115–122, November 2003.
- T. Rakthanmanon, E. Keogh, S. Lonardi, and S. Evans, “Time series epenthesis: clustering time series streams requires ignoring some data,” in Proceedings of the IEEE 11th International Conference on Data Mining (ICDM '11), 2011, http://www.cs.ucr.edu/~stelo/papers/ICDM11_TSE.pdf.
- E. Keogh, X. Xi, L. Wei, and C. A. Ratanamahatana, The UCR time series classication/clustering homepage, http://www.cs.ucr.edu/~eamonn/time_series_data/.
- T. C. Fu, “A review on time series data mining,” Engineering Applications of Artificial Intelligence, vol. 24, no. 1, pp. 164–181, 2011.
- A. K. Jain, “Data clustering: 50 years beyond K-means,” Pattern Recognition Letters, vol. 31, no. 8, pp. 651–666, 2010.
- J. C. Dunn, “A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters,” Journal of Cybernetics, vol. 3, no. 3, pp. 32–57, 1973.
- F. Höppner and F. Klawonn, “Compensation of translational displacement in time series clustering using cross correlation,” in Advances in Intelligent Data Analysis VIII, N. M. Adams, C. Robardet, A. Siebes, and J. F. Boulicaut, Eds., pp. 71–82, Springer, Berlin, Germany, 2009.
- F. Klawonn, “Fuzzy clustering: insights and a new approach,” Mathware & Soft Computing, vol. 11, no. 2-3, pp. 125–142, 2004.
- R. Killick, I. A. Eckley, K. Ewans, and P. Jonathan, “Detection of changes in variance of oceanographic time-series using changepoint analysis,” Ocean Engineering, vol. 37, no. 13, pp. 1120–1126, 2010.
- S. Eschrich, J. W. Ke, L. O. Hall, and D. B. Goldgof, “Fast accurate fuzzy clustering through data reduction,” IEEE Transactions on Fuzzy Systems, vol. 11, no. 2, pp. 262–270, 2003.
- C. S. Möller-Levet, F. Klawonn, K. H. Cho, and O. Wolkenhauer, “Fuzzy clustering of short time-series and unevenly distributed sampling points,” in Advances in Intelligent Data Analysis V, vol. 2810 of Lecture Notes in Computer Science, pp. 330–340, 2003.
- E. N. Nasibov and S. Peker, “Time series labeling algorithms based on the K-nearest neighbors' frequencies,” Expert Systems with Applications, vol. 38, no. 5, pp. 5028–5035, 2011.
- S. R. Kannan, S. Ramathilagam, and P. C. Chung, “Effective fuzzy c-means clustering algorithms for data clustering problems,” Expert Systems With Applications, vol. 39, pp. 6292–6300, 2012.
- J. Mennis and D. S. Guo, “Spatial data mining and geographic knowledge discovery—an introduction,” Computers, Environment and Urban Systems, vol. 33, no. 6, pp. 403–408, 2009.
- M. F. Macchiato, L. la Rotonda, V. Lapenna, and M. Ragosta, “Time modelling and spatial clustering of daily ambient temperature: an application in southern Italy,” Environmetrics, vol. 6, no. 1, pp. 31–53, 1995.
- P. S. P. Cowpertwait and T. F. Cox, “Clustering population means under heterogeneity of variance with an application to a rainfall time series problem,” The Statistician, vol. 41, no. 1, pp. 113–121, 1992.
- I. Horenko, “On clustering of non-stationary meteorological time series,” Dynamics of Atmospheres and Oceans, vol. 49, no. 2-3, pp. 164–187, 2010.
- N. Y. Wang and S. M. Chen, “Temperature prediction and TAIFEX forecasting based on automatic clustering techniques and two-factors high-order fuzzy time series,” Expert Systems with Applications, vol. 36, no. 2, pp. 2143–2154, 2009.
- A. M. Alonso, J. R. Berrendero, A. Hernández, and A. Justel, “Time series clustering based on forecast densities,” Computational Statistics & Data Analysis, vol. 51, no. 2, pp. 762–776, 2006.