{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T12:56:13Z","timestamp":1726059373597},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030352301"},{"type":"electronic","value":"9783030352318"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"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":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-35231-8_13","type":"book-chapter","created":{"date-parts":[[2019,11,16]],"date-time":"2019-11-16T00:30:38Z","timestamp":1573864238000},"page":"185-194","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clustering Noisy Temporal Data"],"prefix":"10.1007","author":[{"given":"Paul","family":"Grant","sequence":"first","affiliation":[]},{"given":"Md Zahidul","family":"Islam","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,11,15]]},"reference":[{"key":"13_CR1","unstructured":"Vlachos, M., Lin, J., Keogh, E., Gunopulos, D.: Wavelet-based anytime algorithm for k-means clustering of time series. In: Proceedings of Workshop on Clustering High Dimensionality Data and Its Applications (2003)"},{"key":"13_CR2","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1016\/j.knosys.2014.08.011","volume":"71","author":"MA Rahman","year":"2014","unstructured":"Rahman, M.A., Islam, M.Z.: A hybrid clustering technique combining a novel genetic algorithm with k-means. Knowl. Based Syst. (KBS) 71, 345\u2013365 (2014)","journal-title":"Knowl. Based Syst. (KBS)"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Beg, A.H., Islam, M.Z.: A novel genetic algorithm-based clustering technique and its suitability for knowledge discovery from a brain dataset. In: Proceedings of IEEE Congress on Evolutionary Computation (IEEE CEC), Vancouver, Canada, 24\u201329 July 2016, pp. 948\u2013956 (2016)","DOI":"10.1109\/CEC.2016.7743892"},{"key":"13_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-45171-7","volume-title":"Applied Multivariate Statistical Analysis","author":"WK H\u00e4rdle","year":"2015","unstructured":"H\u00e4rdle, W.K., Simar, L.: Applied Multivariate Statistical Analysis. Springer, Heidelberg (2015). \nhttps:\/\/doi.org\/10.1007\/978-3-662-45171-7"},{"key":"13_CR5","unstructured":"R Core Team, R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2013). \nhttp:\/\/www.R-project.org\/"},{"key":"13_CR6","unstructured":"Constantine, W., Percival, D.: wmtsa: Wavelet Methods for Time Series Analysis. R package version 2.0-3 (2017). \nhttps:\/\/CRAN.R-project.org\/package=wmtsa"},{"issue":"4","key":"13_CR7","first-page":"315","volume":"1","author":"A Goyal","year":"2012","unstructured":"Goyal, A., Bijalwan, A., Chowdhury, K.: A comprehensive review of image smoothing techniques. Int. J. Adv. Res. Comput. Sci. Technol. 1(4), 315\u2013319 (2012)","journal-title":"Int. J. Adv. Res. Comput. Sci. Technol."},{"key":"13_CR8","doi-asserted-by":"publisher","first-page":"1857","DOI":"10.1016\/j.patcog.2005.01.025","volume":"38","author":"T Warren Liao","year":"2005","unstructured":"Warren Liao, T.: Clustering of time series data-a survey. J. Pattern Recogn. Soc. 38, 1857\u20131874 (2005)","journal-title":"J. Pattern Recogn. Soc."},{"key":"13_CR9","volume-title":"Introduction to Wavelets and Wavelet Transforms","author":"C Sidney Burrus","year":"1998","unstructured":"Sidney Burrus, C., Gopinath, R., Guo, H.: Introduction to Wavelets and Wavelet Transforms. Prentice Hall, New Jersey (1998)"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Guo, H., Liu, Y., Liang, H., Gao, X.: An application on time series clustering based on wavelet decomposition and denoising. In: Fourth International Conference on Natural Computation (2008)","DOI":"10.1109\/ICNC.2008.311"},{"issue":"2","key":"13_CR11","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1109\/99.388960","volume":"2","author":"A Graps","year":"1995","unstructured":"Graps, A.: An introduction to wavelets. IEEE Comput. Sci. Eng. 2(2), 50\u201361 (1995)","journal-title":"IEEE Comput. Sci. Eng."},{"key":"13_CR12","unstructured":"Polikar, R.: The Engineers Ultimate Guide to Wavelet Analysis: The Wavelet Tutorial Part I (2006)"},{"key":"13_CR13","volume-title":"Wavelet Methods for Time Series Analysis, Cambridge Series in Statistical and Probabilistic Mathematics","author":"DB Percival","year":"2006","unstructured":"Percival, D.B., Walden, A.T.: Wavelet Methods for Time Series Analysis, Cambridge Series in Statistical and Probabilistic Mathematics. Cambridge University Press, Cambridge (2006)"},{"key":"13_CR14","series-title":"Use R!","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-75961-6","volume-title":"Wavelet Methods in Statistics with R","author":"GP Nason","year":"2008","unstructured":"Nason, G.P.: Wavelet Methods in Statistics with R. Use R!. Springer, New York (2008). \nhttps:\/\/doi.org\/10.1007\/978-0-387-75961-6"},{"key":"13_CR15","doi-asserted-by":"publisher","first-page":"2558","DOI":"10.1109\/78.709546","volume":"46","author":"T Downie","year":"1998","unstructured":"Downie, T., Silverman, B.: The discrete multiple wavelet transform and thresholding methods. IEEE Trans Signal Process. 46, 2558\u20132561 (1998)","journal-title":"IEEE Trans Signal Process."},{"key":"13_CR16","doi-asserted-by":"publisher","first-page":"1200","DOI":"10.1080\/01621459.1995.10476626","volume":"90","author":"D Donoho","year":"1995","unstructured":"Donoho, D., Johnstone, I.: Adapting to unknown smoothness via wavelet shrinkage. Am. Stat. Asoc. 90, 1200\u20131224 (1995)","journal-title":"Am. Stat. Asoc."},{"key":"13_CR17","unstructured":"MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: 5-th Berkeley Symposium on Mathematical Statistics and Probability, pp. 291\u2013297 (1967)"},{"key":"13_CR18","unstructured":"Murtagh, F.: Multidimensional clustering algorithms. In: COMPSTAT Lectures 4. Wuerzburg: Physica-Verlag (1985)"},{"key":"13_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"420","DOI":"10.1007\/3-540-44503-X_27","volume-title":"Database Theory \u2014 ICDT 2001","author":"CC Aggarwal","year":"2001","unstructured":"Aggarwal, C.C., Hinneburg, A., Keim, D.A.: On the surprising behavior of distance metrics in high dimensional space. In: Van den Bussche, J., Vianu, V. (eds.) ICDT 2001. LNCS, vol. 1973, pp. 420\u2013434. Springer, Heidelberg (2001). \nhttps:\/\/doi.org\/10.1007\/3-540-44503-X_27"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-35231-8_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,11,16]],"date-time":"2019-11-16T00:38:26Z","timestamp":1573864706000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-35231-8_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030352301","9783030352318"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-35231-8_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"15 November 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Dalian","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 November 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/adma2019.neusoft.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"170","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"39","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"26","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"7","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}