{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,4]],"date-time":"2025-04-04T09:06:29Z","timestamp":1743757589738,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031263866"},{"type":"electronic","value":"9783031263873"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-26387-3_2","type":"book-chapter","created":{"date-parts":[[2023,3,16]],"date-time":"2023-03-16T15:03:10Z","timestamp":1678978990000},"page":"21-37","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["CDPS: Constrained DTW-Preserving Shapelets"],"prefix":"10.1007","author":[{"given":"Hussein El","family":"Amouri","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Lampert","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pierre","family":"Gan\u00e7arski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cl\u00e9ment","family":"Mallet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,17]]},"reference":[{"issue":"03","key":"2_CR1","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1142\/S0219622020500133","volume":"19","author":"B Cai","year":"2020","unstructured":"Cai, B., Huang, G., Xiang, Y., Angelova, M., Guo, L., Chi, C.H.: Multi-scale shapelets discovery for time-series classification. Int. J. Inf. Technol. Decis. Mak 19(03), 721\u2013739 (2020)","journal-title":"Int. J. Inf. Technol. Decis. Mak"},{"key":"2_CR2","unstructured":"Dau, H.A., et al.: The UCR time series classification archive (October 2018). https:\/\/www.cs.ucr.edu\/~eamonn\/time_series_data_2018\/"},{"key":"2_CR3","unstructured":"Davidson, I., Ravi, S.: Identifying and generating easy sets of constraints for clustering. In: AAAI Conference on Artificial Intelligence (AAAI), pp. 336\u2013341 (2006)"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Grabocka, J., Schilling, N., Wistuba, M., Schmidt-Thieme, L.: Learning time-series shapelets. In: International Conference on Knowledge Discovery & Data Mining (SIGKDD), pp. 392\u2013401 (2014)","DOI":"10.1145\/2623330.2623613"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Hadsell, R., Chopra, S., LeCun, Y.: Dimensionality reduction by learning an invariant mapping. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). vol. 2, pp. 1735\u20131742 (2006)","DOI":"10.1109\/CVPR.2006.100"},{"issue":"4","key":"2_CR6","doi-asserted-by":"publisher","first-page":"851","DOI":"10.1007\/s10618-013-0322-1","volume":"28","author":"J Hills","year":"2014","unstructured":"Hills, J., Lines, J., Baranauskas, E., Mapp, J., Bagnall, A.: Classification of time series by shapelet transformation. Data Min. Knowl. Discov. 28(4), 851\u2013881 (2014)","journal-title":"Data Min. Knowl. Discov."},{"key":"2_CR7","doi-asserted-by":"crossref","unstructured":"Keogh, E., Lonardi, S., Ratanamahatana, C.A.: Towards parameter-free data mining. In: International Conference on Knowledge Discovery & Data Mining (SIGKDD), pp. 206\u2013215 (2004)","DOI":"10.1145\/1014052.1014077"},{"issue":"6","key":"2_CR8","doi-asserted-by":"publisher","first-page":"1663","DOI":"10.1007\/s10618-018-0573-y","volume":"32","author":"T Lampert","year":"2018","unstructured":"Lampert, T., et al.: Constrained distance based clustering for time-series: a comparative and experimental study. Data Min. Knowl. Discov. 32(6), 1663\u20131707 (2018). https:\/\/doi.org\/10.1007\/s10618-018-0573-y","journal-title":"Data Min. Knowl. Discov."},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Lei, Q., Yi, J., Vaculin, R., Wu, L., Dhillon, I.S.: Similarity preserving representation learning for time series clustering. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI), pp. 2845\u20132851 (2017)","DOI":"10.24963\/ijcai.2019\/394"},{"issue":"8","key":"2_CR10","first-page":"707","volume":"10","author":"V Levenshtein","year":"1966","unstructured":"Levenshtein, V.: Binary codes capable of correcting deletions, insertions, and reversals. Sov. Phys. Dokl. 10(8), 707\u2013710 (1966)","journal-title":"Sov. Phys. Dokl."},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"Li, Z., Liu, J., Tang, X.: Constrained clustering via spectral regularization. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 421\u2013428. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206852"},{"key":"2_CR12","doi-asserted-by":"crossref","unstructured":"Lines, J., Davis, L.M., Hills, J., Bagnall, A.: A shapelet transform for time series classification. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 289\u2013297 (2012)","DOI":"10.1145\/2339530.2339579"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Lods, A., Malinowski, S., Tavenard, R., Amsaleg, L.: Learning DTW-preserving shapelets. In: International Symposium on Intelligent Data Analysis (IDA) (2017)","DOI":"10.1007\/978-3-319-68765-0_17"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Mueen, A., Keogh, E., Young, N.: Logical-shapelets: an expressive primitive for time series classification. In: Proceedings of ACM SIGKDD: International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 1154\u20131162 (2011)","DOI":"10.1145\/2020408.2020587"},{"issue":"11","key":"2_CR15","doi-asserted-by":"publisher","first-page":"1762","DOI":"10.14778\/3342263.3342648","volume":"12","author":"J Paparrizos","year":"2019","unstructured":"Paparrizos, J., Franklin, M.J.: GRAIL: efficient time-series representation learning. VLDB Endowment 12(11), 1762\u20131777 (2019)","journal-title":"VLDB Endowment"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Paparrizos, J., Gravano, L.: k-shape: Efficient and accurate clustering of time series. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data (SIGMOD), pp. 1855\u20131870 (2015)","DOI":"10.1145\/2723372.2737793"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Paparrizos, J., Liu, C., Elmore, A.J., Franklin, M.J.: Debunking four long-standing misconceptions of time-series distance measures. In: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (ACM SIGMOD), pp. 1887\u20131905 (2020)","DOI":"10.1145\/3318464.3389760"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Rakthanmanon, T., Keogh, E.: Fast shapelets: A scalable algorithm for discovering time series shapelets. In: Proceedings of the 2013 SIAM International Conference on Data Mining (SDM), pp. 668\u2013676 (2013)","DOI":"10.1137\/1.9781611972832.74"},{"key":"2_CR19","unstructured":"Sakoe, H., Chiba, S.: Dynamic-programming approach to continuous speech recognition. In: Proceedings of the International Cartographic Association ICA, pp. 65\u201369 (1971)"},{"issue":"1","key":"2_CR20","doi-asserted-by":"publisher","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 Tans. Acoust. Speech Signal Process. 26(1), 43\u201349 (1978)","journal-title":"IEEE Tans. Acoust. Speech Signal Process."},{"key":"2_CR21","doi-asserted-by":"crossref","unstructured":"Shah, M., Grabocka, J., Schilling, N., Wistuba, M., Schmidt-Thieme, L.: Learning DTW-shapelets for time-series classification. In: Proceedings of the 3rd IKDD Conference on Data Science (ACM IKDD CODS), pp. 1\u20138 (2016)","DOI":"10.1145\/2888451.2888456"},{"key":"2_CR22","unstructured":"Sperandio, R.C.: Recherche de s\u00e9ries temporelles \u00e0 l\u2019aide de DTW-preserving shapelets. Ph.D. thesis, Universit\u00e9 Rennes 1 (2019)"},{"key":"2_CR23","doi-asserted-by":"crossref","unstructured":"Tiano, D., Bonifati, A., Ng, R.: Feature-driven time series clustering. In: 24th International Conference on Extending Database Technology (EDBT), pp. 349\u2013354 (2021)","DOI":"10.1145\/3448016.3452757"},{"key":"2_CR24","doi-asserted-by":"crossref","unstructured":"Ulanova, L., Begum, N., Keogh, E.: Scalable clustering of time series with u-shapelets. In: Proceedings of the 2015 SIAM International Conference on Data Mining (SDM), pp. 900\u2013908 (2015)","DOI":"10.1137\/1.9781611974010.101"},{"issue":"1","key":"2_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00778-004-0144-2","volume":"15","author":"M Vlachos","year":"2006","unstructured":"Vlachos, M., Hadjieleftheriou, M., Gunopulos, D., Keogh, E.: Indexing multidimensional time-series. VLDB J. 15(1), 1\u201320 (2006)","journal-title":"VLDB J."},{"key":"2_CR26","unstructured":"Wagstaff, K., Basu, S., Davidson, I.: When is constrained clustering beneficial, and why? In: AAAI Conference on Artificial Intelligence (IAAI) (2006)"},{"key":"2_CR27","unstructured":"Wagstaff, K., Cardie, C., Rogers, S., Schr\u00f6dl, S.: Constrained k-means clustering with background knowledge. In: Proceedings of the Eighteenth International Conference on Machine Learning (ICML). vol. 1, pp. 577\u2013584 (2001)"},{"key":"2_CR28","unstructured":"Wu, L., Yen, I.E.H., Yi, J., Xu, F., Lei, Q., Witbrock, M.: Random warping series: a random features method for time-series embedding. In: 21st International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 793\u2013802 (2018)"},{"key":"2_CR29","doi-asserted-by":"crossref","unstructured":"Yamaguchi, A., Maya, S., Maruchi, K., Ueno, K.: LTSpAUC: learning time-series shapelets for optimizing partial AUC. In: Proceedings of the 2020 SIAM International Conference on Data Mining (SDM), pp. 1\u20139 (2020)","DOI":"10.1137\/1.9781611976236.1"},{"key":"2_CR30","doi-asserted-by":"crossref","unstructured":"Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD), pp. 947\u2013956 (2009)","DOI":"10.1145\/1557019.1557122"},{"issue":"1","key":"2_CR31","doi-asserted-by":"publisher","first-page":"149","DOI":"10.1007\/s10618-010-0179-5","volume":"22","author":"L Ye","year":"2011","unstructured":"Ye, L., Keogh, E.: Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data Min. Knowl. Discov. 22(1), 149\u2013182 (2011)","journal-title":"Data Min. Knowl. Discov."},{"key":"2_CR32","doi-asserted-by":"crossref","unstructured":"Zakaria, J., Mueen, A., Keogh, E.: Clustering time series using unsupervised-shapelets. In: 2012 IEEE 12th International Conference on Data Mining (ICDM), pp. 785\u2013794 (2012)","DOI":"10.1109\/ICDM.2012.26"},{"issue":"1","key":"2_CR33","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/s10618-015-0411-4","volume":"30","author":"J Zakaria","year":"2016","unstructured":"Zakaria, J., Mueen, A., Keogh, E., Young, N.: Accelerating the discovery of unsupervised-shapelets. Data Min. Knowl. Discov. 30(1), 243\u2013281 (2016)","journal-title":"Data Min. Knowl. Discov."},{"key":"2_CR34","unstructured":"Zhang, Q., Wu, J., Yang, H., Tian, Y., Zhang, C.: Unsupervised feature learning from time series. In: International Joint Conferences on Artificial Intelligence (IJCAI), pp. 2322\u20132328 (2016)"},{"key":"2_CR35","doi-asserted-by":"crossref","unstructured":"Zheng, G., Yang, Y., Carbonell, J.: Efficient shift-invariant dictionary learning. In: International Conference on Knowledge Discovery & Data Mining ACM SIGKDD, pp. 2095\u20132104 (2016)","DOI":"10.1145\/2939672.2939824"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-26387-3_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T15:01:40Z","timestamp":1729090900000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-26387-3_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031263866","9783031263873"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-26387-3_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"17 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grenoble","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2022.ecmlpkdd.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1060","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":"236","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":"0","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":"22% - 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":"3-4","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":"3-4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"17 demo track papers have been accepted from 28 submissions","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}