{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T21:59:44Z","timestamp":1742939984083,"version":"3.40.3"},"publisher-location":"Cham","reference-count":44,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030181284"},{"type":"electronic","value":"9783030181291"}],"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\/978-3-030-18129-1_5","type":"book-chapter","created":{"date-parts":[[2019,6,20]],"date-time":"2019-06-20T03:24:18Z","timestamp":1561001058000},"page":"89-118","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Discriminant Chronicle Mining"],"prefix":"10.1007","author":[{"given":"Yann","family":"Dauxais","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Gross-Amblard","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Guyet","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andr\u00e9","family":"Happe","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,6,21]]},"reference":[{"issue":"2","key":"5_CR1","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1007\/s10115-011-0408-2","volume":"31","author":"A Achar","year":"2012","unstructured":"Achar, A., Laxman, S., & Sastry, P. (2012). A unified view of the apriori-based algorithms for frequent episode discovery. Knowledge and Information Systems, 31(2), 223\u2013250.","journal-title":"Knowledge and Information Systems"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Agrawal, R. & Srikant, R. (1995). Mining sequential patterns. In Proceedings of the International Conference on Data Engineering, pp. 3\u201314. IEEE.","DOI":"10.1109\/ICDE.1995.380415"},{"issue":"2","key":"5_CR3","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1016\/0004-3702(84)90008-0","volume":"23","author":"JF Allen","year":"1984","unstructured":"Allen, J. F. (1984). Towards a general theory of action and time. Artificial Intelligence, 23(2), 123\u2013154.","journal-title":"Artificial Intelligence"},{"issue":"3","key":"5_CR4","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.artmed.2013.03.006","volume":"58","author":"MR Alvarez","year":"2013","unstructured":"Alvarez, M. R., Felix, P., & Carinena, P. (2013). Discovering metric temporal constraint networks on temporal databases. Artificial Intelligence in Medicine, 58(3), 139\u2013154.","journal-title":"Artificial Intelligence in Medicine"},{"issue":"4","key":"5_CR5","first-page":"63","volume":"4","author":"I Batal","year":"2013","unstructured":"Batal, I., Valizadegan, H., Cooper, G. F., & Hauskrecht, M. (2013). A temporal pattern mining approach for classifying electronic health record data. ACM Transactions on Intelligent Systems and Technology (TIST), 4(4), 63.","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"issue":"3","key":"5_CR6","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1023\/A:1011429418057","volume":"5","author":"SD Bay","year":"2001","unstructured":"Bay, S. D., & Pazzani, M. J. (2001). Detecting group differences: Mining contrast sets. Data Mining and Knowledge Discovery, 5(3), 213\u2013246.","journal-title":"Data Mining and Knowledge Discovery"},{"key":"5_CR7","doi-asserted-by":"crossref","unstructured":"Berlingerio, M., Bonchi, F., Giannotti, F., & Turini, F. (2007). Mining clinical data with a temporal dimension: A case study. In Proceedings of the International Conference on Bioinformatics and Biomedicine, pp. 429\u2013436.","DOI":"10.1109\/BIBM.2007.42"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Bornemann, L., Lecerf, J., & Papapetrou, P. (2016). STIFE: A framework for feature-based classification of sequences of temporal intervals. In International Conference on Discovery Science, pp. 85\u2013100. Springer, Cham.","DOI":"10.1007\/978-3-319-46307-0_6"},{"key":"5_CR9","unstructured":"Bringmann, B., Nijssen, S., & Zimmermann, A. (2011). Pattern-based classification: a unifying perspective. arXiv preprint \n                    arXiv:1111.6191\n                    \n                  ."},{"key":"5_CR10","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/B978-1-55860-377-6.50023-2","volume-title":"Machine Learning Proceedings 1995","author":"William W. Cohen","year":"1995","unstructured":"Cohen, W. W. (1995). Fast effective rule induction. In Proceedings of the International Conference on Machine Learning, pp. 115\u2013123."},{"key":"5_CR11","doi-asserted-by":"crossref","unstructured":"Concaro, S., Sacchi, L., Cerra, C., Fratino, P., & Bellazzi, R. (2009). Mining healthcare data with temporal association rules: Improvements and assessment for a practical use. In Conference on Artificial Intelligence in Medicine in Europe, pp. 16\u201325.","DOI":"10.1007\/978-3-642-02976-9_3"},{"issue":"4","key":"5_CR12","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. (2012). A complete chronicle discovery approach: Application to activity analysis. Expert Systems, 29(4), 321\u2013346.","journal-title":"Expert Systems"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Dauxais, Y., Guyet, T., Gross-Amblard, D., & Happe, A. (2017). Discriminant chronicles mining: Application to care pathways analytics. In Proceedings of the Conference on Artificial Intelligence in Medicine, pp. 234\u2013244. Springer, Cham.","DOI":"10.1007\/978-3-319-59758-4_26"},{"key":"5_CR14","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/0004-3702(91)90006-6","volume":"49","author":"R Dechter","year":"1991","unstructured":"Dechter, R., Meiri, I., & Pearl, J. (1991). Temporal constraint networks. Artificial Intelligence, 49, 61\u201395.","journal-title":"Artificial Intelligence"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"Dong, G., & Li, J. (1999). Efficient mining of emerging patterns: Discovering trends and differences. In Proceedings of ACM SIGKDD, pp. 43\u201352.","DOI":"10.1145\/312129.312191"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Doran, G., & Ray, S. (2014). A theoretical and empirical analysis of support vector machine methods for multiple-instance classification. Machine Learning, 97(1), 79\u2013102.","DOI":"10.1007\/s10994-013-5429-5"},{"key":"5_CR17","unstructured":"Dousson, C., & Duong, T. V. (1999). Discovering chronicles with numerical time constraints from alarm logs for monitoring dynamic systems. In Proceedings of International Conference on Artificial Intelligence, pp. 620\u2013626."},{"issue":"1","key":"5_CR18","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1007\/s10618-015-0403-4","volume":"30","author":"W Duivesteijn","year":"2016","unstructured":"Duivesteijn, W., Feelders, A. J., & Knobbe, A. (2016). Exceptional model mining. Data Mining and Knowledge Discovery, 30(1), 47\u201398.","journal-title":"Data Mining and Knowledge Discovery"},{"key":"5_CR19","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., Braud, A., Bringay, S., Grac, C., Le Ber, F., Levet, D., et al. (2014). Discriminant temporal patterns for linking physico-chemistry and biology in hydro-ecosystem assessment. Ecological Informatics, 24, 210\u2013221.","journal-title":"Ecological Informatics"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Fabr\u00e8gue, M., Braud, A., Bringay, S., Le\u00a0Ber, F., & Teisseire, M. (2013). Orderspan: Mining closed partially ordered patterns. In International Symposium on Intelligent Data Analysis, pp. 186\u2013197. Springer, Heidelberg.","DOI":"10.1007\/978-3-642-41398-8_17"},{"issue":"01","key":"5_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1017\/S026988890999035X","volume":"25","author":"J Foulds","year":"2010","unstructured":"Foulds, J., & Frank, E. (2010). A review of multi-instance learning assumptions. The Knowledge Engineering Review, 25(01), 1\u201325.","journal-title":"The Knowledge Engineering Review"},{"issue":"3","key":"5_CR22","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. (2015). Mining sequential patterns for classification. Knowledge and Information Systems, 45(3), 731\u2013749.","journal-title":"Knowledge and Information Systems"},{"key":"5_CR23","unstructured":"Guyet, T., & Quiniou, R. (2011). Extracting temporal patterns from interval-based sequences. In Proceedings of International Joint Conference on Artificial Intelligence, pp. 1306\u20131311."},{"issue":"3","key":"5_CR24","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1007\/s10115-010-0356-2","volume":"29","author":"F Herrera","year":"2011","unstructured":"Herrera, F., Carmona, C. J., Gonz\u00e1lez, P., & Del Jesus, M. J. (2011). An overview on subgroup discovery: Foundations and applications. Knowledge and Information Systems, 29(3), 495\u2013525.","journal-title":"Knowledge and Information Systems"},{"issue":"1","key":"5_CR25","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.artmed.2012.06.002","volume":"56","author":"Z Huang","year":"2012","unstructured":"Huang, Z., Lu, X., & Duan, H. (2012). On mining clinical pathway patterns from medical behaviors. Artificial Intelligence in Medicine, 56(1), 35\u201350.","journal-title":"Artificial Intelligence in Medicine"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Lakshmanan, G.\u00a0T., Rozsnyai, S., & Wang, F. (2013). Investigating clinical care pathways correlated with outcomes. In Business process management, pp. 323\u2013338. Springer, Heidelberg.","DOI":"10.1007\/978-3-642-40176-3_27"},{"key":"5_CR27","first-page":"1","volume":"2","author":"AD Lattner","year":"2003","unstructured":"Lattner, A. D., Kim, S., Cervone, G., & Grefenstette, J. J. (2003). Experimental comparison of symbolic learning programs for the classification of gene network topology models. Center for Computing Technologies-TZI, 2, 1.","journal-title":"Center for Computing Technologies-TZI"},{"key":"5_CR28","unstructured":"Lipton, Z.\u00a0C. (2016). The mythos of model interpretability. arXiv preprint \n                    arXiv:1606.03490\n                    \n                  ."},{"issue":"1","key":"5_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1824795.1824798","volume":"43","author":"NR Mabroukeh","year":"2010","unstructured":"Mabroukeh, N. R., & Ezeife, C. I. (2010). A taxonomy of sequential pattern mining algorithms. ACM Journal of Computing Survey, 43(1), 1\u201341.","journal-title":"ACM Journal of Computing Survey"},{"issue":"3","key":"5_CR30","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1023\/A:1009748302351","volume":"1","author":"H Mannila","year":"1997","unstructured":"Mannila, H., Toivonen, H., & Inkeri Verkamo, A. (1997). Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1(3), 259\u2013289.","journal-title":"Data Mining and Knowledge Discovery"},{"key":"5_CR31","unstructured":"M\u00e4ntyj\u00e4rvi, J., Himberg, J., Kangas, P., Tuomela, U., & Huuskonen, P. (2004). Sensor signal data set for exploring context recognition of mobile devices. In Proceedings of 2nd International Conference on Pervasive Computing (PERVASIVE 2004), pp. 18\u201323."},{"issue":"2","key":"5_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2431211.2431218","volume":"45","author":"CH Mooney","year":"2013","unstructured":"Mooney, C. H., & Roddick, J. F. (2013). Sequential pattern mining\u2014approaches and algorithms. ACM Journal of Computing Survey, 45(2), 1\u201339.","journal-title":"ACM Journal of Computing Survey"},{"issue":"1","key":"5_CR33","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1007\/s10115-013-0707-x","volume":"42","author":"R Moskovitch","year":"2015","unstructured":"Moskovitch, R., & Shahar, Y. (2015). Fast time intervals mining using the transitivity of temporal relations. Knowledge and Information Systems, 42(1), 21\u201348.","journal-title":"Knowledge and Information Systems"},{"issue":"6","key":"5_CR34","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.revmed.2014.11.009","volume":"36","author":"G Moulis","year":"2015","unstructured":"Moulis, G., Lapeyre-Mestre, M., Palmaro, A., Pugnet, G., Montastruc, J.-L., & Sailler, L. (2015). French health insurance databases: What interest for medical research? La Revue de M\u00e9decine Interne, 36(6), 411\u2013417.","journal-title":"La Revue de M\u00e9decine Interne"},{"key":"5_CR35","first-page":"377","volume":"10","author":"PK Novak","year":"2009","unstructured":"Novak, P. K., Lavra\u010d, N., & Webb, G. I. (2009). Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining. Journal of Machine Learning Research, 10, 377\u2013403.","journal-title":"Journal of Machine Learning Research"},{"key":"5_CR36","doi-asserted-by":"crossref","unstructured":"Papapetrou, P., Kollios, G., Sclaroff, S., & Gunopulos, D. (2005). Discovering frequent arrangements of temporal intervals. In Fifth IEEE International Conference on Data Mining, pp. 8\u2013pp. IEEE.","DOI":"10.1109\/ICDM.2005.50"},{"key":"5_CR37","doi-asserted-by":"crossref","unstructured":"Pei, J., Han, J., & Wang, W. (2002). Mining sequential patterns with constraints in large databases. In Proceedings of the International Conference on Information and Knowledge Management, pp. 18\u201325. ACM.","DOI":"10.1145\/584792.584799"},{"issue":"11","key":"5_CR38","doi-asserted-by":"publisher","first-page":"1161","DOI":"10.1002\/pds.3879","volume":"24","author":"E Polard","year":"2015","unstructured":"Polard, E., Nowak, E., Happe, A., Biraben, A., & Oger, E. (2015). Brand name to generic substitution of antiepileptic drugs does not lead to seizure-related hospitalization: A population-based case-crossover study. Pharmacoepidemiology and Drug Safety, 24(11), 1161\u20131169.","journal-title":"Pharmacoepidemiology and Drug Safety"},{"key":"5_CR39","doi-asserted-by":"crossref","unstructured":"Quiniou, R., Cordier, M., Carrault, G., & Wang, F. (2001). Application of ILP to cardiac arrhythmia characterization for chronicle recognition. In Proceedings of International Conference on Inductive Logic Programming, pp. 220\u2013227.","DOI":"10.1007\/3-540-44797-0_18"},{"key":"5_CR40","doi-asserted-by":"crossref","unstructured":"Sahugu\u00e8de, A., Fergani, S., Le\u00a0Corronc, E., & Le\u00a0Lann, M.-V. (2018). Mapping chronicles to a k-dimensional Euclidean space via random projections. In 14th International Conference on Automation Science and Engineering (CASE), 6p. IEEE.","DOI":"10.1109\/COASE.2018.8560562"},{"key":"5_CR41","unstructured":"Santisteban, J. & Tejada-C\u00e1rcamo, J. (2015). Unilateral Jaccard similarity coefficient. In GSB@ SIGIR, pp. 23\u201327."},{"issue":"12","key":"5_CR42","doi-asserted-by":"publisher","first-page":"1371","DOI":"10.1109\/34.735811","volume":"20","author":"T Starner","year":"1998","unstructured":"Starner, T., Weaver, J., & Pentland, A. (1998). Real-time american sign language recognition using desk and wearable computer based video. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(12), 1371\u20131375.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"5_CR43","unstructured":"Uno, T., Kiyomi, M., & Arimura, H. (2004). LCM ver. 2: Efficient mining algorithms for frequent\/closed\/maximal itemsets. In FIMI, vol. 126."},{"key":"5_CR44","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.jbi.2014.09.003","volume":"53","author":"AP Wright","year":"2015","unstructured":"Wright, A. P., Wright, A. T., McCoy, A. B., & Sittig, D. F. (2015). The use of sequential pattern mining to predict next prescribed medications. Journal of Biomedical Informatics, 53, 73\u201380.","journal-title":"Journal of Biomedical Informatics"}],"container-title":["Studies in Computational Intelligence","Advances in Knowledge Discovery and Management"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-18129-1_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,21]],"date-time":"2019-06-21T15:06:26Z","timestamp":1561129586000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-18129-1_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030181284","9783030181291"],"references-count":44,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-18129-1_5","relation":{},"ISSN":["1860-949X","1860-9503"],"issn-type":[{"type":"print","value":"1860-949X"},{"type":"electronic","value":"1860-9503"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"21 June 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}