{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T06:03:53Z","timestamp":1725516233268},"publisher-location":"Berlin, Heidelberg","reference-count":29,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783642334856"},{"type":"electronic","value":"9783642334863"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012]]},"DOI":"10.1007\/978-3-642-33486-3_17","type":"book-chapter","created":{"date-parts":[[2012,9,10]],"date-time":"2012-09-10T16:39:17Z","timestamp":1347295157000},"page":"260-276","source":"Crossref","is-referenced-by-count":3,"title":["A Bayesian Scoring Technique for Mining Predictive and Non-Spurious Rules"],"prefix":"10.1007","author":[{"given":"Iyad","family":"Batal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gregory","family":"Cooper","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Milos","family":"Hauskrecht","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"unstructured":"Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the International Conference on Very Large Data Bases, VLDB (1994)","key":"17_CR1"},{"key":"17_CR2","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1023\/A:1011429418057","volume":"5","author":"S.D. Bay","year":"2001","unstructured":"Bay, S.D., Pazzani, M.J.: Detecting group differences: Mining contrast sets. Data Mining and Knowledge Discovery\u00a05, 213\u2013246 (2001)","journal-title":"Data Mining and Knowledge Discovery"},{"doi-asserted-by":"crossref","unstructured":"Bayardo, R.J.: Constraint-based rule mining in large, dense databases. In: Proceedings of the International Conference on Data Engineering, ICDE (1999)","key":"17_CR3","DOI":"10.1109\/ICDE.1999.754924"},{"issue":"1","key":"17_CR4","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\u00a057(1), 289\u2013300 (1995)","journal-title":"Journal of the Royal Statistical Society"},{"doi-asserted-by":"crossref","unstructured":"Brin, S., Motwani, R., Silverstein, C.: Beyond market baskets: generalizing association rules to correlations. In: Proceedings of the International Conference on Management of Data, SIGMOD (1997)","key":"17_CR5","DOI":"10.1145\/253260.253327"},{"doi-asserted-by":"crossref","unstructured":"Cheng, H., Yan, X., Han, J., Wei Hsu, C.: Discriminative frequent pattern analysis for effective classification. In: Proceedings of the International Conference on Data Engineering, ICDE (2007)","key":"17_CR6","DOI":"10.1109\/ICDE.2007.367917"},{"doi-asserted-by":"crossref","unstructured":"Clark, P., Niblett, T.: The cn2 induction algorithm. Machine Learning (1989)","key":"17_CR7","DOI":"10.1007\/BF00116835"},{"doi-asserted-by":"crossref","unstructured":"Cohen, W.: Fast effective rule induction. In: Proceedings of International Conference on Machine Learning, ICML (1995)","key":"17_CR8","DOI":"10.1016\/B978-1-55860-377-6.50023-2"},{"unstructured":"Cohen, W., Singer, Y.: A simple, fast, and effective rule learner. In: Proceedings of the National Conference on Artificial Intelligence, AAAI (1999)","key":"17_CR9"},{"key":"17_CR10","doi-asserted-by":"publisher","first-page":"1036","DOI":"10.1109\/TKDE.2005.127","volume":"17","author":"M. Deshpande","year":"2005","unstructured":"Deshpande, M., Kuramochi, M., Wale, N., Karypis, G.: Frequent substructure-based approaches for classifying chemical compounds. IEEE Transactions on Knowledge and Data Engineering\u00a017, 1036\u20131050 (2005)","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"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, SIGKDD (1999)","key":"17_CR11","DOI":"10.1145\/312129.312191"},{"key":"17_CR12","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/j.datak.2008.05.007","volume":"66","author":"T.P. Exarchos","year":"2008","unstructured":"Exarchos, T.P., Tsipouras, M.G., Papaloukas, C., Fotiadis, D.I.: A two-stage methodology for sequence classification based on sequential pattern mining and optimization. Data and Knowledge Engineering\u00a066, 467\u2013487 (2008)","journal-title":"Data and Knowledge Engineering"},{"unstructured":"Fayyad, U., Irani, K.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Proceedings of the International Joint Conference on Artificial Intelligence, IJCAI (1993)","key":"17_CR13"},{"doi-asserted-by":"crossref","unstructured":"Geng, L., Hamilton, H.J.: Interestingness measures for data mining: A survey. ACM Computing Surveys\u00a038 (2006)","key":"17_CR14","DOI":"10.1145\/1132960.1132963"},{"doi-asserted-by":"crossref","unstructured":"Grosskreutz, H., Boley, M., Krause-Traudes, M.: Subgroup discovery for election analysis: a case study in descriptive data mining. In: Proceedings of the International Conference on Discovery Science (2010)","key":"17_CR15","DOI":"10.1007\/978-3-642-16184-1_5"},{"doi-asserted-by":"crossref","unstructured":"Heckerman, D., Geiger, D., Chickering, D.M.: Learning bayesian networks: The combination of knowledge and statistical data. Machine Learning (1995)","key":"17_CR16","DOI":"10.1016\/B978-1-55860-377-6.50079-7"},{"issue":"7","key":"17_CR17","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1080\/08839510600779688","volume":"20","author":"B. Kavsek","year":"2006","unstructured":"Kavsek, 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":"17_CR18","series-title":"Lecture Notes in Artificial Intelligence","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1007\/11615576_12","volume-title":"Constraint-Based Mining and Inductive Databases","author":"N. Lavra\u010d","year":"2006","unstructured":"Lavra\u010d, N., Gamberger, D.: Relevancy in Constraint-Based Subgroup Discovery. In: Boulicaut, J.-F., De Raedt, L., Mannila, H. (eds.) Constraint-Based Mining. LNCS (LNAI), vol.\u00a03848, pp. 243\u2013266. Springer, Heidelberg (2006)"},{"key":"17_CR19","series-title":"Lecture Notes in Artificial Intelligence","doi-asserted-by":"publisher","first-page":"364","DOI":"10.1007\/3-540-45357-1_39","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"J. Li","year":"2001","unstructured":"Li, J., Shen, H., Topor, R.: Mining Optimal Class Association Rule Set. In: Cheung, D., Williams, G.J., Li, Q. (eds.) PAKDD 2001. LNCS (LNAI), vol.\u00a02035, p. 364. Springer, Heidelberg (2001)"},{"unstructured":"Li, W., Han, J., Pei, J.: CMAR: Accurate and efficient classification based on multiple class-association rules. In: Proceedings of the International Conference on Data Mining, ICDM (2001)","key":"17_CR20"},{"unstructured":"Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Knowledge Discovery and Data Mining, pp. 80\u201386 (1998)","key":"17_CR21"},{"doi-asserted-by":"crossref","unstructured":"Nijssen, S., Guns, T., De Raedt, L.: Correlated itemset mining in roc space: a constraint programming approach. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining, SIGKDD (2009)","key":"17_CR22","DOI":"10.1145\/1557019.1557092"},{"key":"17_CR23","first-page":"377","volume":"10","author":"P.K. Novak","year":"2009","unstructured":"Novak, P.K., Lavra\u010d, N., Webb, G.I.: Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining. Journal of Machine Learning Research (JMLR)\u00a010, 377\u2013403 (2009)","journal-title":"Journal of Machine Learning Research (JMLR)"},{"doi-asserted-by":"crossref","unstructured":"Sebastiani, F.: Machine learning in automated text categorization. ACM Computing Surveys (2002)","key":"17_CR24","DOI":"10.1145\/505282.505283"},{"doi-asserted-by":"crossref","unstructured":"Smyth, P., Goodman, R.M.: An information theoretic approach to rule induction from databases. IEEE Transactions on Knowledge and Data Engineering (1992)","key":"17_CR25","DOI":"10.1109\/69.149926"},{"issue":"1","key":"17_CR26","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10994-007-5006-x","volume":"68","author":"G.I. Webb","year":"2007","unstructured":"Webb, G.I.: Discovering significant patterns. Machine Learning\u00a068(1), 1\u201333 (2007)","journal-title":"Machine Learning"},{"doi-asserted-by":"crossref","unstructured":"Xin, D., Cheng, H., Yan, X., Han, J.: Extracting redundancy-aware top-k patterns. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining, SIGKDD (2006)","key":"17_CR27","DOI":"10.1145\/1150402.1150452"},{"doi-asserted-by":"crossref","unstructured":"Yang, Y., Webb, G.I., Wu, X.: Discretization methods. In: The Data Mining and Knowledge Discovery Handbook, pp. 113\u2013130. Springer (2005)","key":"17_CR28","DOI":"10.1007\/0-387-25465-X_6"},{"key":"17_CR29","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1109\/69.846291","volume":"12","author":"M.J. Zaki","year":"2000","unstructured":"Zaki, M.J.: Scalable algorithms for association mining. IEEE Transaction on Knowledge and Data Engineering (TKDE)\u00a012, 372\u2013390 (2000)","journal-title":"IEEE Transaction on Knowledge and Data Engineering (TKDE)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-33486-3_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,29]],"date-time":"2024-04-29T02:49:02Z","timestamp":1714358942000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-642-33486-3_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2012]]},"ISBN":["9783642334856","9783642334863"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-642-33486-3_17","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2012]]}}}