{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T04:19:02Z","timestamp":1742962742927,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319574530"},{"type":"electronic","value":"9783319574547"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","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":[[2017]]},"DOI":"10.1007\/978-3-319-57454-7_59","type":"book-chapter","created":{"date-parts":[[2017,4,22]],"date-time":"2017-04-22T12:09:31Z","timestamp":1492862971000},"page":"762-772","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Local-to-Global Unsupervised Anomaly Detection from Temporal Data"],"prefix":"10.1007","author":[{"given":"Seif-Eddine","family":"Benkabou","sequence":"first","affiliation":[]},{"given":"Khalid","family":"Benabdeslem","sequence":"additional","affiliation":[]},{"given":"Bruno","family":"Canitia","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,4,23]]},"reference":[{"key":"59_CR1","unstructured":"Bahadori, M., Kale, D., Yingying, F., Yan, L.: Functional subspace clustering with application to time series. In: Proceedings of ICML, pp. 228\u2013237 (2015)"},{"issue":"1","key":"59_CR2","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1109\/TSMCC.2008.2007248","volume":"39","author":"S Budalakoti","year":"2009","unstructured":"Budalakoti, S., Srivastava, A., Otey, M.: Anomaly detection and diagnosis algorithms for discrete symbol sequences with applications to airline safety. IEEE Trans. Syst. Man Cybern. Part C: Appl. 39(1), 101\u2013113 (2009)","journal-title":"IEEE Trans. Syst. Man Cybern. Part C: Appl."},{"key":"59_CR3","doi-asserted-by":"crossref","unstructured":"Chandola, V., Mithal, V., Kumar, V.: Comparative evaluation of anomaly detection techniques for sequence data. In: Proceedings of ICDM, pp. 743\u2013748 (2008)","DOI":"10.1109\/ICDM.2008.151"},{"key":"59_CR4","unstructured":"Chen, Y., Keogh, E., Hu, B., Begum, N., Bagnall, A., Mueen, A., Batista, G.: The UCR time series classification archive (2015). www.cs.ucr.edu\/eamonn\/time_series_data\/"},{"key":"59_CR5","first-page":"1","volume":"7","author":"J Dem\u0161ar","year":"2006","unstructured":"Dem\u0161ar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1\u201330 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"59_CR6","volume-title":"Nonparametric Functional Data Analysis: Theory and Practice","author":"F Ferraty","year":"2006","unstructured":"Ferraty, F., Vieu, P.: Nonparametric Functional Data Analysis: Theory and Practice. Springer, Heidelberg (2006)"},{"key":"59_CR7","unstructured":"G\u00f6rnitz, N., Braun, L., Kloft, M.: Hidden Markov anomaly detection. In: Proceedings of ICML, pp. 1833\u20131842 (2015)"},{"issue":"9","key":"59_CR8","doi-asserted-by":"publisher","first-page":"2250","DOI":"10.1109\/TKDE.2013.184","volume":"26","author":"M Gupta","year":"2014","unstructured":"Gupta, M., Gao, J., Aggarwal, C., Han, J.: Outlier detection for temporal data: a survey. IEEE Trans. Knowl. Data Eng. 26(9), 2250\u20132267 (2014)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"59_CR9","doi-asserted-by":"crossref","unstructured":"Hautamaki, T., Nykanen, P., Frant, P.: Time-series clustering by approximate prototypes. In: Proceedings of ICPR, pp. 1\u20134 (2008)","DOI":"10.1109\/ICPR.2008.4761105"},{"key":"59_CR10","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1109\/TPAMI.2005.95","volume":"27","author":"J Huang","year":"2005","unstructured":"Huang, J., Ng, M., Rong, H., Li, Z.: Automated variable weighting in k-means type clustering. IEEE Trans. Pattern Anal. Mach. Intell. 27, 657\u2013668 (2005)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"8","key":"59_CR11","doi-asserted-by":"publisher","first-page":"1026","DOI":"10.1109\/TKDE.2007.1048","volume":"19","author":"L Jing","year":"2007","unstructured":"Jing, L., Ng, M., Huang, Z.: An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data. IEEE Trans. Knowl. Data Eng. 19(8), 1026\u20131041 (2007)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"59_CR12","unstructured":"Lane, T., Brodley, C.: Sequence matching and learning in anomaly detection for computer security. In: AAAI Workshop: AI Approaches to Fraud Detection and Risk Management, pp. 43\u201349 (1997)"},{"key":"59_CR13","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1023\/A:1024016609528","volume":"52","author":"D Modha","year":"2003","unstructured":"Modha, D., Spangler, S.: Feature weighting in k-means clustering. Mach. Learn. 52, 217\u2013237 (2003)","journal-title":"Mach. Learn."},{"key":"59_CR14","unstructured":"Ng, A., Jordan, M., Weiss, Y.: Analysis and an algorithm. In: Proceedings of Neural Information Processing Systems (NIPS), pp. 849\u2013856. MIT Press (2002)"},{"key":"59_CR15","doi-asserted-by":"crossref","unstructured":"Petitjean, F., Forestier, G., Webb, G., Nicholson, A., Chen, Y., Keogh, E.: Dynamic time warping averaging of time series allows faster, more accurate classification. In: Proceedings of ICDM, pp. 470\u2013479 (2014)","DOI":"10.1109\/ICDM.2014.27"},{"key":"59_CR16","unstructured":"Portnoy, L., Eskin, E., Stolfo, S.: Intrusion detection with unlabeled data using clustering. In: Proceedings of ACM CSS Workshop on Data Mining Applied to Security (DMSA), pp. 5\u20138 (2001)"},{"issue":"5","key":"59_CR17","doi-asserted-by":"crossref","first-page":"561","DOI":"10.3233\/IDA-2007-11508","volume":"11","author":"S Salvador","year":"2007","unstructured":"Salvador, S., Chan, P.: Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11(5), 561\u2013580 (2007)","journal-title":"Intell. Data Anal."},{"key":"59_CR18","unstructured":"Sch\u00f6lkopf, B., Williamson, R., Smola, A., Shawe-Taylor, J., Platt, J.: Support vector method for novelty detection. In: Proceedings of Neural Information Processing Systems (NIPS), pp. 582\u2013588 (1999)"},{"issue":"1","key":"59_CR19","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1007\/BF01074755","volume":"4","author":"T Vintsyuk","year":"1968","unstructured":"Vintsyuk, T.: Speech discrimination by dynamic programming. Cybernetics 4(1), 52\u201357 (1968)","journal-title":"Cybernetics"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-57454-7_59","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T16:20:23Z","timestamp":1710346823000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-57454-7_59"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319574530","9783319574547"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-57454-7_59","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]},"assertion":[{"value":"23 April 2017","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jeju","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2017","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 May 2017","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2017","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2017","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/pakdd2017.snu.ac.kr\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}