{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:27:38Z","timestamp":1743049658571,"version":"3.40.3"},"publisher-location":"Berlin, Heidelberg","reference-count":18,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783642539312"},{"type":"electronic","value":"9783642539329"}],"license":[{"start":{"date-parts":[[2013,1,1]],"date-time":"2013-01-01T00:00:00Z","timestamp":1356998400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013]]},"DOI":"10.1007\/978-3-642-53932-9_53","type":"book-chapter","created":{"date-parts":[[2013,12,20]],"date-time":"2013-12-20T00:54:07Z","timestamp":1387500847000},"page":"541-552","source":"Crossref","is-referenced-by-count":0,"title":["Granular Sketch Based Uncertain Time Series Streams Clustering"],"prefix":"10.1007","author":[{"given":"Jingyu","family":"Chen","sequence":"first","affiliation":[]},{"given":"Ping","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Xian\u2019gang","family":"Sheng","sequence":"additional","affiliation":[]}],"member":"297","reference":[{"key":"53_CR1","series-title":"LNAI","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1007\/11731139_24","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"M. Chau","year":"2006","unstructured":"Chau, M., Cheng, R., Kao, B., Ng, J.: Uncertain data mining: An example in clustering location data. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol.\u00a03918, pp. 199\u2013204. Springer, Heidelberg (2006)"},{"key":"53_CR2","doi-asserted-by":"crossref","unstructured":"Gaffney, S., Smyth, P.: Trajectory clustering with mixtures of regression models. In: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Diego, CA, USA, pp. 63\u201372 (August 1999)","DOI":"10.1145\/312129.312198"},{"key":"53_CR3","unstructured":"Xiong, Y., Yeung, D.: Mixtures of ARMA models for model-based time series clustering. In: Proceedings of the 2002 IEEE International Conference on Data Mining, Maebashi City, Japan, pp. 717\u2013720 (December 2002)"},{"key":"53_CR4","doi-asserted-by":"crossref","unstructured":"Sathe, S., Jeung, H., Aberer, K.: Creating probabilistic databases from imprecise time-series data. In: Proceedings of the 2011 IEEE International Conference on Data Engineering (ICDE), pp. 327\u2013338 (2011)","DOI":"10.1109\/ICDE.2011.5767838"},{"key":"53_CR5","doi-asserted-by":"crossref","unstructured":"Ackermann, M.R., Lammersen, C., Martens, M., Raupach, C., Swierkot, K., Sohler, C.: StreamKM++: A Clustering Algorithm for Data Streams. Journal of Experimental Algorithmics (JEA)\u00a017(1) (July 2012)","DOI":"10.1145\/2133803.2184450"},{"key":"53_CR6","doi-asserted-by":"crossref","unstructured":"Tran, T.T.L., Peng, P., Li, B.D., Diao, Y., Liu, A.N.: PODS: a new model and processing algorithms for uncertain data streams. In: Proceedings of the 2010 International Conference on Management of Data, Indiana, USA, pp. 159\u2013170 (2010)","DOI":"10.1145\/1807167.1807187"},{"key":"53_CR7","volume-title":"Principle and Algorithm of Data Mining","author":"F. Shao","year":"2003","unstructured":"Shao, F., Yu, Z.: Principle and Algorithm of Data Mining. Water conservancy & water electric press of China, Beijing (2003)"},{"key":"53_CR8","doi-asserted-by":"crossref","unstructured":"Li, Y., Han, J., Yang, J.: Clustering Moving Objects. In: Proc. of the 10th ACM SIGKDD Int\u2019l. Conf. on Knowledge Discovery and Data Mining (2004)","DOI":"10.1145\/1014052.1014129"},{"key":"53_CR9","doi-asserted-by":"crossref","unstructured":"Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: an efficient data clustering method for very large databases. In: Proc. of the 1996 ACM SIGMOD Int\u2019l. Conf. on Management of Data (1996)","DOI":"10.1145\/233269.233324"},{"key":"53_CR10","doi-asserted-by":"crossref","unstructured":"Luhr, S., Lazarescu, M.: Incremental clustering on dynamic data streams using connectivity based representative points. Data & Knowledge Engineering, 1\u201327 (2009)","DOI":"10.1016\/j.datak.2008.08.006"},{"key":"53_CR11","doi-asserted-by":"crossref","unstructured":"Alon, N., Matias, Y., Szegedy, M.: The Space Complexity of Approximating the Frequency Moments. In: ACM Symposium on Theory of Computing, pp. 20\u201329 (1996)","DOI":"10.1145\/237814.237823"},{"issue":"1","key":"53_CR12","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1016\/j.jalgor.2003.12.001","volume":"55","author":"G. Cormode","year":"2005","unstructured":"Cormode, G., Muthukrishnan, S.: An Improved Data-Stream Summary: The Count-min Sketch and its Applications. Journal of Algorithms\u00a055(1), 58\u201375 (2005)","journal-title":"Journal of Algorithms"},{"key":"53_CR13","doi-asserted-by":"crossref","unstructured":"Cormode, G., Muthukrishnan, S.: What\u2019s hot and what\u2019s not: Tracking most frequent items dynamically. In: Proceedings of the 22nd ACM Symposium on Principles of Database Systems, pp. 296\u2013306 (2003)","DOI":"10.1145\/773153.773182"},{"key":"53_CR14","unstructured":"Manerikar, N., Palpanas, T.: Frequent items in streaming data: An experimental evaluation of the state-of-the-art. Technical Report DISI-08-017, University of Trento (March 2008)"},{"key":"53_CR15","doi-asserted-by":"crossref","unstructured":"Aggarwal, C.: A Framework for Clustering Massive-Domain Data Streams. In: IEEE 25th International Conference on Data Engineering (ICDE 2009), pp. 102\u2013113 (2009)","DOI":"10.1109\/ICDE.2009.13"},{"key":"53_CR16","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, L., Guan, Y.: Sketch-based Streaming PCA Algorithm for Network-wide Traffic Anomaly Detection. In: Proc. ICDCS (2010)","DOI":"10.1109\/ICDCS.2010.45"},{"key":"53_CR17","doi-asserted-by":"crossref","unstructured":"Somasundaram, R.S., Nedunchezhian, R.: Evaluation of three Simple Imputation Methods for Enhancing Preprocessing of Data with Missing Values. International Journal of Computer Applications\u00a021(10) (May 2011) 0975\u20138887","DOI":"10.5120\/2619-3544"},{"key":"53_CR18","doi-asserted-by":"crossref","unstructured":"Stoica, I., Morris, R., Karger, D., Kaashoek, M.F., Balakrishnan, H.: Chord: A scalable peer-to-peer lookup service for internet applications. In: Proceedings of the 2001 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, pp. 149\u2013160. ACM Press (2001)","DOI":"10.1145\/964723.383071"}],"container-title":["Communications in Computer and Information Science","Information Computing and Applications"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-53932-9_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,25]],"date-time":"2019-05-25T13:45:33Z","timestamp":1558791933000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-642-53932-9_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013]]},"ISBN":["9783642539312","9783642539329"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-642-53932-9_53","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2013]]}}}