{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T04:41:19Z","timestamp":1772858479172,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2015,7,1]],"date-time":"2015-07-01T00:00:00Z","timestamp":1435708800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. Comput. Sci. Technol."],"published-print":{"date-parts":[[2015,7]]},"DOI":"10.1007\/s11390-015-1565-7","type":"journal-article","created":{"date-parts":[[2015,7,8]],"date-time":"2015-07-08T04:51:22Z","timestamp":1436331082000},"page":"859-873","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Enhancing Time Series Clustering by Incorporating Multiple Distance Measures with Semi-Supervised Learning"],"prefix":"10.1007","volume":"30","author":[{"given":"Jing","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shan-Feng","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaodi","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanchun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2015,7,8]]},"reference":[{"key":"1565_CR1","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1109\/ICDM.2006.33"},{"key":"1565_CR2","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/2124295.2124358"},{"key":"1565_CR3","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1007\/978-981-4585-18-7_28"},{"key":"1565_CR4","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1016\/j.pocean.2011.11.005"},{"issue":"9","key":"1565_CR5","doi-asserted-by":"crossref","first-page":"6319","DOI":"10.1016\/j.eswa.2010.02.089","volume":"37","author":"CP Lai","year":"2010","unstructured":"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.","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"1565_CR6","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1007\/s10618-005-0039-x","volume":"13","author":"X Wang","year":"2006","unstructured":"Wang X, Smith K, Hyndman R. Characteristic-based clustering for time series data. Data Mining and Knowledge Discovery, 2006, 13(3): 335-364.","journal-title":"Data Mining and Knowledge Discovery"},{"issue":"9","key":"1565_CR7","doi-asserted-by":"crossref","first-page":"11891","DOI":"10.1016\/j.eswa.2011.03.081","volume":"38","author":"X Zhang","year":"2011","unstructured":"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.","journal-title":"Expert Systems with Applications"},{"key":"1565_CR8","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1109\/ICDM.2012.26"},{"issue":"2\/3","key":"1565_CR9","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1007\/s10994-005-5825-6","volume":"58","author":"A Bagnall","year":"2005","unstructured":"Bagnall A, Janacek G. Clustering time series with clipped data. Machine Learning, 2005, 58(2\/3): 151-178.","journal-title":"Machine Learning"},{"issue":"2","key":"1565_CR10","doi-asserted-by":"crossref","first-page":"1542","DOI":"10.14778\/1454159.1454226","volume":"1","author":"H Ding","year":"2008","unstructured":"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.","journal-title":"Proc. the VLDB Endowment"},{"key":"1565_CR11","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/956750.956777"},{"key":"1565_CR12","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/1557019.1557122"},{"key":"1565_CR13","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1137\/1.9781611972719.1"},{"issue":"9","key":"1565_CR14","doi-asserted-by":"crossref","first-page":"2231","DOI":"10.1016\/j.patcog.2010.09.022","volume":"44","author":"YS Jeong","year":"2011","unstructured":"Jeong Y S, Jeong M K, Omitaomu O A. Weighted dynamic time warping for time series classification. Pattern Recognition, 2011, 44(9): 2231-2240.","journal-title":"Pattern Recognition"},{"issue":"6","key":"1565_CR15","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1109\/TNNLS.2014.2333876","volume":"26","author":"PF Marteau","year":"2015","unstructured":"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.","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"2","key":"1565_CR16","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1109\/TPAMI.2008.76","volume":"31","author":"PF Marteau","year":"2009","unstructured":"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.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"1565_CR17","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/1459359.1459417"},{"key":"1565_CR18","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.neucom.2014.01.045","volume":"138","author":"Y Sun","year":"2014","unstructured":"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.","journal-title":"Neurocomputing"},{"issue":"2","key":"1565_CR19","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1007\/s11280-013-0256-y","volume":"18","author":"J Qi","year":"2015","unstructured":"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.","journal-title":"World Wide Web"},{"key":"1565_CR20","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1007\/978-3-540-24741-8_8"},{"key":"1565_CR21","doi-asserted-by":"crossref","unstructured":"Hautamaki V, Nykanen P, Franti P. Time-series clustering by approximate prototypes. In Proc. the 19th International Conference Pattern Recognition, December 2008.","DOI":"10.1109\/ICPR.2008.4761105"},{"key":"1565_CR22","unstructured":"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."},{"issue":"3","key":"1565_CR23","doi-asserted-by":"crossref","first-page":"2741","DOI":"10.3390\/ijerph110302741","volume":"11","author":"S Ghassempour","year":"2014","unstructured":"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.","journal-title":"International Journal of Environmental Research and Public Health"},{"key":"1565_CR24","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1016\/j.engappai.2014.12.015","volume":"39","author":"H Izakian","year":"2015","unstructured":"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.","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"1","key":"1565_CR25","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/A:1013635829250","volume":"47","author":"M Ramoni","year":"2002","unstructured":"Ramoni M, Sebastiani P, Cohen P. Bayesian clustering by dynamics. Machine Learning, 2002, 47(1): 91-121.","journal-title":"Machine Learning"},{"issue":"2","key":"1565_CR26","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1109\/TKDE.2010.112","volume":"23","author":"Y Yang","year":"2011","unstructured":"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.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"2","key":"1565_CR27","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1109\/TSMCC.2010.2052608","volume":"41","author":"Y Yang","year":"2011","unstructured":"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.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews"},{"key":"1565_CR28","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1137\/1.9781611973440.60"},{"issue":"1","key":"1565_CR29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10994-008-5084-4","volume":"74","author":"B Kulis","year":"2009","unstructured":"Kulis B, Basu S, Dhillon I, Mooney R. Semi-supervised graph clustering: A kernel approach. Machine Learning, 2009, 74(1): 1-22.","journal-title":"Machine Learning"},{"key":"1565_CR30","doi-asserted-by":"crossref","unstructured":"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 \u2014 Volume Part I, April 2012, pp.197-212.","DOI":"10.1007\/978-3-642-29038-1_16"},{"issue":"3","key":"1565_CR31","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1007\/s10115-008-0134-6","volume":"17","author":"Y Chen","year":"2008","unstructured":"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.","journal-title":"Knowledge and Information Systems"},{"issue":"3","key":"1565_CR32","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1016\/j.patcog.2011.08.020","volume":"45","author":"M Shiga","year":"2012","unstructured":"Shiga M, Mamitsuka H. Efficient semi-supervised learning on locally informative multiple graphs. Pattern Recognition, 2012, 45(3): 1035-1049.","journal-title":"Pattern Recognition"},{"key":"1565_CR33","unstructured":"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."},{"issue":"1","key":"1565_CR34","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/TASSP.1978.1163055","volume":"26","author":"H Sakoe","year":"1978","unstructured":"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.","journal-title":"IEEE Transactions on Acoustics, Speech and Signal Processing"},{"issue":"8","key":"1565_CR35","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","volume":"22","author":"J Shi","year":"2000","unstructured":"Shi J, Malik J. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"15","key":"1565_CR36","doi-asserted-by":"crossref","first-page":"1944","DOI":"10.1093\/bioinformatics\/btp338","volume":"25","author":"S Zhu","year":"2009","unstructured":"Zhu S, Zeng J, Mamitsuka H. Enhancing MEDLINE document clustering by incorporating MeSH semantic similarity. Bioinformatics, 2009, 25(15): 1944-1951.","journal-title":"Bioinformatics"},{"key":"1565_CR37","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/1015330.1015414"},{"issue":"2","key":"1565_CR38","first-page":"477","volume":"3","author":"R Ghaemi","year":"2009","unstructured":"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.","journal-title":"World Academy of Science, Engineering and Technology"},{"issue":"11","key":"1565_CR39","doi-asserted-by":"crossref","first-page":"2293","DOI":"10.1016\/j.ins.2011.01.029","volume":"181","author":"X Huang","year":"2011","unstructured":"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.","journal-title":"Information Sciences"},{"issue":"4","key":"1565_CR40","doi-asserted-by":"crossref","first-page":"1265","DOI":"10.1109\/TSMCB.2012.2227998","volume":"43","author":"J Gu","year":"2013","unstructured":"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.","journal-title":"IEEE Transactions on Cybernetics"},{"key":"1565_CR41","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/1148170.1148241"},{"key":"1565_CR42","unstructured":"Ghosh J. Scalable clustering. In Handbook of Data Mining, Ye N (ed.), CRC Press, 2003, pp.247-277."},{"key":"1565_CR43","first-page":"583","volume":"3","author":"A Strehl","year":"2003","unstructured":"Strehl A, Ghosh J. Cluster ensembles \u2014 A knowledge reuse framework for combining multiple partitions. The Journal of Machine Learning Research, 2003, 3: 583-617.","journal-title":"The Journal of Machine Learning Research"}],"container-title":["Journal of Computer Science and Technology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11390-015-1565-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11390-015-1565-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11390-015-1565-7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,27]],"date-time":"2019-08-27T22:42:37Z","timestamp":1566945757000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11390-015-1565-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,7]]},"references-count":43,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2015,7]]}},"alternative-id":["1565"],"URL":"https:\/\/doi.org\/10.1007\/s11390-015-1565-7","relation":{},"ISSN":["1000-9000","1860-4749"],"issn-type":[{"value":"1000-9000","type":"print"},{"value":"1860-4749","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,7]]}}}