|  Hirano S, Tsumoto S. Cluster analysis of time-series medical data based on the trajectory representation and multiscale comparison techniques. In Proc. the 6th International Conference on Data Mining, December 2006, pp.896-901. Ruiz E J, Hristidis V, Castillo C, Gionis A, Jaimes A. Correlating financial time series with micro-blogging activity. In Proc. the 5th ACM International Conference on Web Search and Data Mining, February 2012, pp.513-522. Tan S C, San L J P. Time series clustering: A superior alternative for market basket analysis. In Proc. the 1st International Conference on Advanced Data and Information Engineering, January 2013, pp.241-248. Mackas D L, Greve W, Edwards M et al. Changing zooplankton seasonality in a changing ocean: Comparing time series of zooplankton phenology. Progress in Oceanography, 2012, 97/98/99/100: 31-62. Lai C P, Chung P C, Tseng V S. A novel two-level clustering method for time series data analysis. Expert Systems with Applications, 2010, 37(9): 6319-6326. Wang X, Smith K, Hyndman R. Characteristic-based clustering for time series data. Data Mining and Knowledge Discovery, 2006, 13(3): 335-364. Zhang X, Liu J, Du Y, Lv T. A novel clustering method on time series data. Expert Systems with Applications, 2011, 38(9): 11891-11900. Zakaria J, Mueen A, Keogh E J. Clustering time series using unsupervised-shapelets. In Proc. the 12th IEEE International Conference on Data Mining, December 2012, pp.785-794. Bagnall A, Janacek G. Clustering time series with clipped data. Machine Learning, 2005, 58(2/3): 151-178. Ding H, Trajcevski G, Scheuermann P, Wang X, Keogh E J. Querying and mining of time series data: Experimental comparison of representations and distance measures. Proc. the VLDB Endowment, 2008, 1(2): 1542-1552. Vlachos M, Hadjieleftheriou M, Gunopulos D, Keogh E J. Indexing multi-dimensional time-series with support for multiple distance measures. In Proc. the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 2003, pp.216-225. Ye L, Keogh E J. Time series shapelets: A new primitive for data mining. In Proc. the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, June 28-July 1, 2009, pp.947-956. Keogh E J, Pazzani M J. Derivative dynamic time warping. In Proc. the 1st SIAM International Conference on Data Mining, April 2001, pp.1:1-1:11. Jeong Y S, Jeong M K, Omitaomu O A. Weighted dynamic time warping for time series classification. Pattern Recognition, 2011, 44(9): 2231-2240. Marteau P F, Gibet S. On recursive edit distance kernels with application to time series classification. IEEE Transactions on Neural Networks and Learning Systems, 2015, 26(6): 1121-1133. Marteau P F. Time warp edit distance with stiffness adjustment for time series matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 306- 318. Shao J, Huang Z, Shen H T, Shen J, Zhou X. Distributionbased similarity measures for multi-dimensional point set retrieval applications. In Proc. the 16th ACM International Conference on Multimedia, October 2008, pp.429-438. Sun Y, Li J, Liu J, Sun B, Chow C. An improvement of symbolic aggregate approximation distance measure for time series. Neurocomputing, 2014, 138: 189-198. Qi J, Zhang R, Ramamohanarao K, Wang H,Wen Z,Wu D. Indexable online time series segmentation with error bound guarantee. World Wide Web, 2015, 18(2): 359-401. Lin J, Vlachos M, Keogh E J, Gunopulos D. Iterative incremental clustering of time series. In Proc. the 9th International Conference on Extending Database Technology, March 2004, pp.106-122. Hautamaki V, Nykanen P, Franti P. Time-series clustering by approximate prototypes. In Proc. the 19th International Conference Pattern Recognition, December 2008. Oates T, Firoiu L, Cohen P R. Clustering time series with hidden Markov models and dynamic time warping. In Proc. the IJCAI-99 Workshop on Neural, Symbolic and Reinforcement Learning Methods for Sequence Learning, August 1999, pp.17-21. Ghassempour S, Girosi F, Maeder A. Clustering multivariate time series using hidden Markov models. International Journal of Environmental Research and Public Health, 2014, 11(3): 2741-2763. Izakian H, Pedrycz W, Jamal I. Fuzzy clustering of time series data using dynamic time warping distance. Engineering Applications of Artificial Intelligence, 2015, 39: 235-244. Ramoni M, Sebastiani P, Cohen P. Bayesian clustering by dynamics. Machine Learning, 2002, 47(1): 91-121. Yang Y, Chen K. Temporal data clustering via weighted clustering ensemble with different representations. IEEE Transactions on Knowledge and Data Engineering, 2011, 23(2): 307-320. Yang Y, Chen K. Time series clustering via RPCL network ensemble with different representations. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2011, 41(2): 190-199. Lines J, Bagnall A. Ensembles of elastic distance measures for time series classification. In Proc. the 14th SIAM International Conference on Data Mining, April 2014, pp.524- 532. Kulis B, Basu S, Dhillon I, Mooney R. Semi-supervised graph clustering: A kernel approach. Machine Learning, 2009, 74(1): 1-22. Huang X, Cheng H, Yang J, Yu J X, Fei H, Huan J. Semisupervised clustering of graph objects: A subgraph mining approach. In Proc. the 17th International Conference on Database Systems for Advanced Applications — Volume Part I, April 2012, pp.197-212. Chen Y, Rege M, Dong M, Hua J. Non-negative matrix factorization for semi-supervised data clustering. Knowledge and Information Systems, 2008, 17(3): 355-379. Shiga M, Mamitsuka H. Efficient semi-supervised learning on locally informative multiple graphs. Pattern Recognition, 2012, 45(3): 1035-1049. Sakoe H, Chiba S. A dynamic programming approach to continuous speech recognition. In Proc. the 7th International Congress on Acoustics, August 1971, pp.65-69. Sakoe H, Chiba S. Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech and Signal Processing, 1978, 26(1): 43-49. Shi J, Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905. Zhu S, Zeng J, Mamitsuka H. Enhancing MEDLINE document clustering by incorporating MeSH semantic similarity. Bioinformatics, 2009, 25(15): 1944-1951. Fern X Z, Brodley C E. Solving cluster ensemble problems by bipartite graph partitioning. In Proc. the 21st International Conference on Machine Learning, July 2004, Article No. 36. Ghaemi R, Sulaiman M N, Ibrahim H, Mustapha N. A survey: Clustering ensembles techniques. World Academy of Science, Engineering and Technology, 2009, 3(2): 477-486. Huang X, Zheng X, Yuan W, Wang F, Zhu S. Enhanced clustering of biomedical documents using ensemble nonnegative matrix factorization. Information Sciences, 2011, 181(11): 2293-2302. Gu J, Feng W, Zeng J, Mamitsuka H, Zhu S. Efficient semisupervised MEDLINE document clustering with MeSH-semantic and global-content constraints. IEEE Transactions on Cybernetics, 2013, 43(4): 1265-1276. Ji X, Xu W. Document clustering with prior knowledge. In Proc. the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, August 2006, pp.405-412. Ghosh J. Scalable clustering. In Handbook of Data Mining, Ye N (ed.), CRC Press, 2003, pp.247-277. Strehl A, Ghosh J. Cluster ensembles — A knowledge reuse framework for combining multiple partitions. The Journal of Machine Learning Research, 2003, 3: 583-617.