{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T01:06:14Z","timestamp":1765760774075,"version":"3.48.0"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030657413"},{"type":"electronic","value":"9783030657420"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-65742-0_3","type":"book-chapter","created":{"date-parts":[[2020,12,15]],"date-time":"2020-12-15T02:03:06Z","timestamp":1607997786000},"page":"30-45","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Generalized Chronicles for Temporal Sequence Classification"],"prefix":"10.1007","author":[{"given":"Yann","family":"Dauxais","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Guyet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,12,16]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Asker, L., Bostr\u00f6m, H., Karlsson, I., Papapetrou, P., Zhao, J.: Mining candidates for adverse drug interactions in electronic patient records. In: Proceedings of the International Conference on PErvasive Technologies Related to Assistive Environments (PETRA), pp. 22:1\u201322:4 (2014)","DOI":"10.1145\/2674396.2674420"},{"issue":"1","key":"3_CR2","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1002\/widm.1144","volume":"5","author":"M Atzmueller","year":"2015","unstructured":"Atzmueller, M.: Subgroup discovery. Wiley Interdisc. Rev. Data Min. Knowl. Disc. 5(1), 35\u201349 (2015)","journal-title":"Wiley Interdisc. Rev. Data Min. Knowl. Disc."},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Bringmann, B., Nijssen, S., Zimmermann, A.: Pattern-based classification: a unifying perspective. In: Proceedings of the LeGo Workshop \u201cFrom Local Patterns to Global Models\u201d, p. 10 (2009)","DOI":"10.1007\/978-1-4419-7738-0_6"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Cohen, W.W.: Fast effective rule induction. In: Proceedings of the International Conference on Machine Learning, pp. 115\u2013123 (1995)","DOI":"10.1016\/B978-1-55860-377-6.50023-2"},{"issue":"4","key":"3_CR5","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1111\/j.1468-0394.2011.00591.x","volume":"29","author":"D Cram","year":"2012","unstructured":"Cram, D., Mathern, B., Mille, A.: A complete chronicle discovery approach: application to activity analysis. Expert Syst. 29(4), 321\u2013346 (2012)","journal-title":"Expert Syst."},{"key":"3_CR6","doi-asserted-by":"crossref","unstructured":"Dauxais, Y., Guyet, T., Gross-Amblard, D., Happe, A.: Discriminant chronicles mining - application to care pathways analytics. In: Proceedings of 16th Conference on Artificial Intelligence in Medicine (AIME), pp. 234\u2013244 (2017)","DOI":"10.1007\/978-3-319-59758-4_26"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Dong, G., Li, J.: Efficient mining of emerging patterns: discovering trends and differences. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDD), pp. 43\u201352 (1999)","DOI":"10.1145\/312129.312191"},{"key":"3_CR8","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.ecoinf.2014.09.003","volume":"24","author":"M Fabr\u00e8gue","year":"2014","unstructured":"Fabr\u00e8gue, M., et al.: Discriminant temporal patterns for linking physico-chemistry and biology in hydro-ecosystem assessment. Ecol. Inf. 24, 210\u2013221 (2014)","journal-title":"Ecol. Inf."},{"issue":"3","key":"3_CR9","doi-asserted-by":"publisher","first-page":"731","DOI":"10.1007\/s10115-014-0817-0","volume":"45","author":"D Fradkin","year":"2015","unstructured":"Fradkin, D., M\u00f6rchen, F.: Mining sequential patterns for classification. Knowl. Inf. Syst. 45(3), 731\u2013749 (2015). https:\/\/doi.org\/10.1007\/s10115-014-0817-0","journal-title":"Knowl. Inf. Syst."},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Giannotti, F., Nanni, M., Pedreschi, D.: Efficient mining of temporally annotated sequences. In: Proceedings of the International Conference on Data Mining (ICDM), pp. 348\u2013359 (2006)","DOI":"10.1137\/1.9781611972764.31"},{"key":"3_CR11","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1007\/3-540-44794-6_16","volume-title":"Principles of Data Mining and Knowledge Discovery","author":"F H\u00f6ppner","year":"2001","unstructured":"H\u00f6ppner, F.: Discovery of temporal patterns. In: De Raedt, L., Siebes, A. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 192\u2013203. Springer, Heidelberg (2001). https:\/\/doi.org\/10.1007\/3-540-44794-6_16"},{"key":"3_CR12","unstructured":"Lipton, Z.C., Berkowitz, J., Elkan, C.: A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019 (2015)"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Mabroukeh, N.R., Ezeife, C.I.: A taxonomy of sequential pattern mining algorithms. ACM Comput. Surv. (CSUR) 43(1), 3:1\u20133:41 (2010)","DOI":"10.1145\/1824795.1824798"},{"issue":"3","key":"3_CR14","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1023\/A:1009748302351","volume":"1","author":"H Mannila","year":"1997","unstructured":"Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Min. Knowl. Disc. 1(3), 259\u2013289 (1997). https:\/\/doi.org\/10.1023\/A:1009748302351","journal-title":"Data Min. Knowl. Disc."},{"key":"3_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1007\/978-3-540-75549-4_11","volume-title":"Knowledge Discovery in Inductive Databases","author":"M Nanni","year":"2007","unstructured":"Nanni, M., Rigotti, C.: Extracting trees of quantitative serial episodes. In: D\u017eeroski, S., Struyf, J. (eds.) KDID 2006. LNCS, vol. 4747, pp. 170\u2013188. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-75549-4_11"},{"key":"3_CR16","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"issue":"1","key":"3_CR17","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1145\/234313.234346","volume":"28","author":"JR Quinlan","year":"1996","unstructured":"Quinlan, J.R.: Learning decision tree classifiers. ACM Comput. Surv. (CSUR) 28(1), 71\u201372 (1996)","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"3_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/BFb0014140","volume-title":"Advances in Database Technology \u2014 EDBT 1996","author":"R Srikant","year":"1996","unstructured":"Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Apers, P., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 1\u201317. Springer, Heidelberg (1996). https:\/\/doi.org\/10.1007\/BFb0014140"},{"issue":"4","key":"3_CR19","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1007\/s10489-013-0426-8","volume":"39","author":"S-J Yen","year":"2013","unstructured":"Yen, S.-J., Lee, Y.-S.: Mining non-redundant time-gap sequential patterns. Appl. Intell. 39(4), 727\u2013738 (2013). https:\/\/doi.org\/10.1007\/s10489-013-0426-8","journal-title":"Appl. Intell."}],"container-title":["Lecture Notes in Computer Science","Advanced Analytics and Learning on Temporal Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-65742-0_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,15]],"date-time":"2025-12-15T01:01:53Z","timestamp":1765760513000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-65742-0_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030657413","9783030657420"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-65742-0_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"16 December 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AALTD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Advanced Analytics and Learning on Temporal Data","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ghent","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Belgium","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aaltd2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/project.inria.fr\/aaltd20\/","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":"easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"29","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":"15","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":"52% - 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":"2-3","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":"2-3","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":"This content has been made available to all.","name":"free","label":"Free to read"}]}}