{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,11]],"date-time":"2025-06-11T04:11:23Z","timestamp":1749615083500,"version":"3.41.0"},"publisher-location":"Berlin, Heidelberg","reference-count":16,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783540440376"},{"type":"electronic","value":"9783540456810"}],"license":[{"start":{"date-parts":[[2002,1,1]],"date-time":"2002-01-01T00:00:00Z","timestamp":1009843200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2002,1,1]],"date-time":"2002-01-01T00:00:00Z","timestamp":1009843200000},"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":[[2002]]},"DOI":"10.1007\/3-540-45681-3_14","type":"book-chapter","created":{"date-parts":[[2007,10,19]],"date-time":"2007-10-19T11:03:25Z","timestamp":1192791805000},"page":"163-175","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Generating Actionable Knowledge by Expert-Guided Subgroup Discovery"],"prefix":"10.1007","author":[{"given":"Dragan","family":"Gamberger","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nada","family":"Lavra\u00e7","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2002,9,18]]},"reference":[{"key":"14_CR1","unstructured":"Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., & Verkamo, A. I. (1996) Fast discovery of association rules. In U. M. Fayyad, G. Piatetski-Shapiro, P. Smyth and R. Uthurusamy (Eds.) Advances in Knowledge Discovery and Data Mining, pp. 307\u2013328. AAAI Press."},{"issue":"4","key":"14_CR2","first-page":"261","volume":"3","author":"P. Clark","year":"1989","unstructured":"Clark, P. & Niblett, T. (1989). The CN2 induction algorithm. Machine Learning, 3(4):261\u2013283.","journal-title":"Machine Learning"},{"key":"14_CR3","unstructured":"Flach, P. & Gamberger, D. (2001) Subgroup evaluation and decision support for direct mailing marketing problem. Integrating Aspects of Data Mining, Decision Support and Meta-Learning Workshop at ECML\/PKDD 2001 Conference."},{"key":"14_CR4","doi-asserted-by":"crossref","unstructured":"Gamberger, D. & Lavra\u00e7, N. (2000) Confirmation rule sets. In Proc. of 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD2000), pp.34\u201343, Springer.","DOI":"10.1007\/3-540-45372-5_4"},{"key":"14_CR5","unstructured":"Gamberger, D. & Lavra\u00e7, N. (2002) Descriptive induction through subgroup discovery: a case study in a medical domain. In Proc. of 19th International Conference on Machine Learning (ICML2002), Morgan Kaufmann, in press."},{"key":"14_CR6","doi-asserted-by":"publisher","first-page":"385","DOI":"10.1080\/095400996116839","volume":"8","author":"T. Kagan","year":"1996","unstructured":"Kagan, T. & Ghosh, J. (1996) Error correlation and error reduction in ensemble classifiers. Connection Science, 8, 385\u2013404.","journal-title":"Connection Science"},{"key":"14_CR7","unstructured":"Kl\u00f6sgen, W. (1996) Explora: A multipattern and multistrategy discovery assistant. In U. M. Fayyad, G. Piatetski-Shapiro, P. Smyth and R. Uthurusamy (Eds.) Advances in Knowledge Discovery and Data Mining, pp. 249\u2013271. MIT Press."},{"key":"14_CR8","doi-asserted-by":"crossref","unstructured":"Krsta\u00e7i\u0107, G., Gamberger, D., & \u0160muc, T. (2001) Coronary heart disease patient models based on inductive machine learning. In Proc. of 8th Conference on Artificial Intelligence in Medicine in Europe (AIME 2001), pp.113\u2013116.","DOI":"10.1007\/3-540-48229-6_15"},{"key":"14_CR9","unstructured":"Lavra\u00e7, N., Gamberger, D., & Flach, P. (2002) Subgroup discovery for actionable knowledge generation: Defiences of classification rule learning and lessons learned. Data Mining Lessons Learned Workshop at ICML 2002 Conference, to be printed."},{"key":"14_CR10","unstructured":"Michalski, R. S., Mozeti\u00e7, I., Hong, J., & Lavra\u00e7, N. (1986) The multi-purpose incremental learning system AQ15 and its testing application on three medical domains. In Proc. Fifth National Conference on Artificial Intelligence, pp. 1041\u20131045, Morgan Kaufmann."},{"issue":"3","key":"14_CR11","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1023\/A:1007601015854","volume":"42","author":"F. Provost","year":"2001","unstructured":"Provost, F. & Fawcett, T. (2001) Robust classification for imprecise environments. Machine Learning, 42(3), 203\u2013231.","journal-title":"Machine Learning"},{"key":"14_CR12","unstructured":"Rivest, R. L. & Sloan, R. (1988) Learning complicated concepts reliably and usefully. In Proc. Workshop on Computational Learning Theory, 69\u201379, Morgan Kaufman."},{"key":"14_CR13","unstructured":"Piatetshy-Shapiro, G. & Matheus, C. J. (1994) The interestingness of deviation. In Proc. of the AAAI-94 Workshop on Knowledge Discovery in Databases, pp. 25\u201336."},{"key":"14_CR14","unstructured":"Silberschatz, A. & Tuzhilin, A. (1995) On Subjective Measure of Interestingness in Knowledge Discovery. In Proc. First International Conference on Knowledge Discovery and Data Mining (KDD), 275\u2013281."},{"key":"14_CR15","doi-asserted-by":"crossref","unstructured":"Todorovski, L., Flach, P., & Lavra\u00e7, N. (2000) Predictive Performance of Weighted Relative Accuracy. In Zighed, D. A., Komorowski, J. and Zytkow, J., editors, Proc. of the 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD2000), Springer-Verlag, 255\u2013264.","DOI":"10.1007\/3-540-45372-5_25"},{"key":"14_CR16","doi-asserted-by":"crossref","unstructured":"Wrobel, S. (1997) An algorithm for multi-relational discovery of subgroups. In Proc. First European Symposium on Principles of Data Mining and Knowledge Discovery, pp.78\u201387, Springer.","DOI":"10.1007\/3-540-63223-9_108"}],"container-title":["Lecture Notes in Computer Science","Principles of Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/3-540-45681-3_14","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T16:35:58Z","timestamp":1749573358000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/3-540-45681-3_14"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2002]]},"ISBN":["9783540440376","9783540456810"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/3-540-45681-3_14","relation":{},"ISSN":["0302-9743"],"issn-type":[{"type":"print","value":"0302-9743"}],"subject":[],"published":{"date-parts":[[2002]]},"assertion":[{"value":"18 September 2002","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}