{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:44:12Z","timestamp":1742913852898,"version":"3.40.3"},"publisher-location":"Berlin, Heidelberg","reference-count":14,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783642029752"},{"type":"electronic","value":"9783642029769"}],"license":[{"start":{"date-parts":[[2009,1,1]],"date-time":"2009-01-01T00:00:00Z","timestamp":1230768000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009]]},"DOI":"10.1007\/978-3-642-02976-9_39","type":"book-chapter","created":{"date-parts":[[2009,7,11]],"date-time":"2009-07-11T05:06:29Z","timestamp":1247288789000},"page":"265-274","source":"Crossref","is-referenced-by-count":6,"title":["Subgroup Discovery in Data Sets with Multi\u2013dimensional Responses: A Method and a Case Study in Traumatology"],"prefix":"10.1007","author":[{"given":"Lan","family":"Umek","sequence":"first","affiliation":[]},{"given":"Bla\u017e","family":"Zupan","sequence":"additional","affiliation":[]},{"given":"Marko","family":"Toplak","sequence":"additional","affiliation":[]},{"given":"Annie","family":"Morin","sequence":"additional","affiliation":[]},{"given":"Jean-Hugues","family":"Chauchat","sequence":"additional","affiliation":[]},{"given":"Gregor","family":"Makovec","sequence":"additional","affiliation":[]},{"given":"Dragica","family":"Smrke","sequence":"additional","affiliation":[]}],"member":"297","reference":[{"key":"39_CR1","volume-title":"Handbook of data mining and knowledge discovery","author":"D.J. Hand","year":"2002","unstructured":"Hand, D.J.: Handbook of data mining and knowledge discovery. Oxford University Press, Inc., New York (2002)"},{"issue":"11","key":"39_CR2","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1145\/240455.240464","volume":"39","author":"U. Fayyad","year":"1996","unstructured":"Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: The kdd process for extracting useful knowledge from volumes of data. Commun. ACM\u00a039(11), 27\u201334 (1996)","journal-title":"Commun. ACM"},{"key":"39_CR3","doi-asserted-by":"crossref","unstructured":"Lavra\u010d, N., Flach, P., Kav\u0161ek, B., Todorovski, L.: Adapting classification rule induction to subgroup discovery. In: Proceedings of IEEE International Conference on Data Mining, pp. 266\u2013273 (2002)","DOI":"10.1109\/ICDM.2002.1183912"},{"key":"39_CR4","first-page":"153","volume":"5","author":"N. Lavra\u010d","year":"2004","unstructured":"Lavra\u010d, N., Kav\u0161ek, B., Flach, P., Todorovski, L.: Subgroup discovery with CN2-SD. Journal of Machine Learning Research\u00a05, 153\u2013188 (2004)","journal-title":"Journal of Machine Learning Research"},{"key":"39_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1007\/978-3-540-45231-7_22","volume-title":"Advances in Intelligent Data Analysis V","author":"B. Kav\u0161ek","year":"2003","unstructured":"Kav\u0161ek, B., Lavra\u010d, N., Jovanoski, V.: APRIORI-SD: Adapting association rule learning to subgroup discovery. In: R. Berthold, M., Lenz, H.-J., Bradley, E., Kruse, R., Borgelt, C. (eds.) IDA 2003. LNCS, vol.\u00a02810, pp. 230\u2013241. Springer, Heidelberg (2003)"},{"issue":"7","key":"39_CR6","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1080\/08839510600779688","volume":"20","author":"B. Kav\u0161ek","year":"2006","unstructured":"Kav\u0161ek, B., Lavra\u010d, N.: APRIORI-SD: Adapting association rule learning to subgroup discovery. Applied Artificial Intelligence\u00a020(7), 543\u2013583 (2006)","journal-title":"Applied Artificial Intelligence"},{"key":"39_CR7","first-page":"55","volume-title":"Proceedings of the 15th International Conference on Machine Learning","author":"H. Blockeel","year":"1998","unstructured":"Blockeel, H., De Raedt, L., Ramon, J.: Top-down induction of clustering trees. In: Proceedings of the 15th International Conference on Machine Learning, pp. 55\u201363. Morgan Kaufmann, San Francisco (1998)"},{"key":"39_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/11733492_14","volume-title":"Knowledge Discovery in Inductive Databases","author":"B. \u017denko","year":"2006","unstructured":"\u017denko, B., Struyf, J.: Learning predictive clustering rules. In: Bonchi, F., Boulicaut, J.-F. (eds.) KDID 2005. LNCS, vol.\u00a03933, pp. 234\u2013250. Springer, Heidelberg (2006)"},{"key":"39_CR9","doi-asserted-by":"publisher","DOI":"10.1002\/9780470316801","volume-title":"Finding Groups in Data: An Introduction to Cluster Analysis","author":"L. Kaufman","year":"1990","unstructured":"Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley, Chichester (1990)"},{"key":"39_CR10","doi-asserted-by":"crossref","unstructured":"Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a dataset via the gap statistic (2000)","DOI":"10.1111\/1467-9868.00293"},{"issue":"1","key":"39_CR11","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"P. Rousseeuw","year":"1987","unstructured":"Rousseeuw, P.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math.\u00a020(1), 53\u201365 (1987)","journal-title":"J. Comput. Appl. Math."},{"issue":"15","key":"39_CR12","doi-asserted-by":"publisher","first-page":"2948","DOI":"10.1002\/sim.3143","volume":"27","author":"I. Irigoien","year":"2008","unstructured":"Irigoien, I., Arenas, C.: INCA: new statistic for estimating the number of clusters and identifying atypical units. Statistics in Medicine\u00a027(15), 2948\u20132973 (2008)","journal-title":"Statistics in Medicine"},{"issue":"1","key":"39_CR13","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","volume":"57","author":"Y. Benjamini","year":"1995","unstructured":"Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological)\u00a057(1), 289\u2013300 (1995)","journal-title":"Journal of the Royal Statistical Society. Series B (Methodological)"},{"key":"39_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1007\/3-540-57868-4_57","volume-title":"Machine Learning: ECML-94","author":"I. Kononenko","year":"1994","unstructured":"Kononenko, I.: Estimating attributes: Analysis and extensions of relief. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol.\u00a0784, pp. 171\u2013182. Springer, Heidelberg (1994)"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Medicine"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-02976-9_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T12:13:25Z","timestamp":1633695205000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-642-02976-9_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009]]},"ISBN":["9783642029752","9783642029769"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-642-02976-9_39","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2009]]}}}