{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T23:52:01Z","timestamp":1725493921247},"publisher-location":"Berlin, Heidelberg","reference-count":38,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783540737223"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"DOI":"10.1007\/978-3-540-73723-0_21","type":"book-chapter","created":{"date-parts":[[2007,10,29]],"date-time":"2007-10-29T11:33:35Z","timestamp":1193657615000},"page":"411-430","source":"Crossref","is-referenced-by-count":3,"title":["Subgroup Discovery with Linguistic Rules"],"prefix":"10.1007","author":[{"given":"Mar\u00eda Jos\u00e9 del","family":"Jesus","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pedro","family":"Gonz\u00e1lez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco","family":"Herrera","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"21_CR1","doi-asserted-by":"crossref","unstructured":"Agrawal R, Imielinski T, Swami AN (1993) Mining Association Rules between Sets of Items in Large Databases. In: Proceedings of the International Conference on Management of Data (ACM SIGMOD 1995). Washington, DC, pp. 207\u2013216","DOI":"10.1145\/170035.170072"},{"key":"21_CR2","first-page":"307","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"R Agrawal","year":"1996","unstructured":"Agrawal R, Mannila H, Srikant R, Toivonen H, Verkamo I (1996) Fast Discovery of Association Rules. In: Fayyad U, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in Knowledge Discovery and Data Mining. AAAI Press, California, pp. 307\u2013328"},{"key":"21_CR3","unstructured":"Atzmueller M, Puppe F, Buscher H-P (2004) Towards Knowledge-Intensive Subgroup Discovery. In: Proceedings Lernen, Wissensentdeckung und Adaptivit\u00e4t Workshop (LWA\u201804). Berlin, pp. 117\u2013123"},{"key":"21_CR4","unstructured":"Au WH, Chan KCC (1998) An effective algorithm for discovering fuzzy rules in relational databases. In: Proceedings of the IEEE International Conference on Fuzzy Systems (Fuzz IEEE\u201898). Anchorage (USA), pp. 1314\u20131319"},{"key":"21_CR5","doi-asserted-by":"crossref","DOI":"10.1887\/0750308958","volume-title":"Handbook of Evolutionary Computation","author":"T B\u00e4ck","year":"1997","unstructured":"B\u00e4ck T, Fogel D, Michalewicz Z (1997) Handbook of Evolutionary Computation. Oxford University Press, Oxford"},{"issue":"4","key":"21_CR6","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":"21_CR7","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-5184-0","volume-title":"Evolutionary Algorithms for Solving Multi-Objective Problems","author":"CA Coello","year":"2002","unstructured":"Coello CA, Van Veldhuizen DA, Lamont GB (2002) Evolutionary Algorithms for Solving Multi-Objective Problems. Kluwer Academic Publishers, New York"},{"issue":"10\/11","key":"21_CR8","doi-asserted-by":"publisher","first-page":"1025","DOI":"10.1002\/(SICI)1098-111X(199810\/11)13:10\/11<1025::AID-INT9>3.0.CO;2-N","volume":"13","author":"O Cord\u00f2n","year":"1998","unstructured":"Cord\u00f2n O, del Jesus MJ, Herrera F (1998) Genetic Learning of Fuzzy Rule-based Classification Systems Co-operating with Fuzzy Reasoning Methods. International Journal of Intelligent Systems 13 (10\/11): 1025\u20131053","journal-title":"International Journal of Intelligent Systems"},{"key":"21_CR9","doi-asserted-by":"crossref","DOI":"10.1142\/4177","volume-title":"Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases","author":"O Cord\u00f2n","year":"2001","unstructured":"Cord\u00f2n O, Herrera F, Hoffmann F, Magdalena L (2001) Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases. World Scientific, Singapore"},{"key":"21_CR10","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1016\/S0020-0255(02)00264-5","volume":"147","author":"G Chen","year":"2002","unstructured":"Chen G, Wei Q (2002) Fuzzy association rules and the extended mining algorithms. Information Sciences 147: 201\u2013228","journal-title":"Information Sciences"},{"key":"21_CR11","volume-title":"Multi-Objective Optimization using Evolutionary Algorithms","author":"K Deb","year":"2001","unstructured":"Deb K (2001) Multi-Objective Optimization using Evolutionary Algorithms. John Wiley & Sons, Chichester"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Del Jesus MJ, Gonz\u00e1lez P, Herrera F, Mesonero M (2005) Evolutionary Induction of Descriptive Rules in a Market Problem. In Ruan D, Chen G, Kerre E, Wets G (eds) Intelligent Data Mining: Techniques and Applications. Springer Verlag, pp. 267\u2013292","DOI":"10.1007\/11004011_14"},{"key":"21_CR13","unstructured":"Del Jesus MJ, Gonz\u00e1lez P, Herrera F, Mesonero M (Accepted) Evolutionary fuzzy rule induction process for subgroup discovery: a case study in marketing. IEEE Trans. Fuzzy Systems"},{"key":"21_CR14","doi-asserted-by":"publisher","first-page":"250","DOI":"10.1109\/TFUZZ.2004.840130","volume":"13","author":"D Dubois","year":"2005","unstructured":"Dubois D, Prade H, Sudamp T (2005) On the representation, measurement, and discovery of fuzzy associations. IEEE Trans. on Fuzzy Systems 13: 250\u2013262","journal-title":"IEEE Trans. on Fuzzy Systems"},{"issue":"3","key":"21_CR15","first-page":"139","volume":"12","author":"PA Flach","year":"1999","unstructured":"Flach PA, Savnik I (1999) Database dependency discovery: a machine learning approach. AI Communications 12(3): 139\u2013160","journal-title":"AI Communications"},{"key":"21_CR16","unstructured":"Fu AW, Wong MH, Sze SC, Wong WC, Wong WL, Yu WK (1998) Finding fuzzy sets for the mining of fuzzy association rules for numerical at-tributes. In: First International Symposium on Intelligent Data Engineering and Learning (IDEAL\u201898). Hong Kong, pp. 263\u2013268"},{"key":"21_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1023\/A:1015057926707","volume":"17","author":"D Gamberger","year":"2002","unstructured":"Gamberger D, Lavrac N (2002) Expert-guided subgroup discovery: Methodology and application. Journal of Artificial Intelligence Research 17: 1\u201327","journal-title":"Journal of Artificial Intelligence Research"},{"issue":"1","key":"21_CR18","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/S0933-3657(03)00034-4","volume":"28","author":"D Gamberger","year":"2003","unstructured":"Gamberger D, Lavrac N, Krstacic G (2003) Active subgroup mining: a case study in coronary heart disease risk group detection. Artificial Intelligence in Medicine 28 (1): 27\u201357","journal-title":"Artificial Intelligence in Medicine"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Hong TP, Chen CH, Wu YL, Lee YC (2004) Using divide-and-conquer GA strategy in fuzzy data mining. In: Ninth International Symposium on Computers and Communications (ISCC 2004). Alexandria, EGYPT, pp. 116\u2013121","DOI":"10.1109\/ISCC.2004.1358391"},{"key":"21_CR20","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1016\/S0165-0114(02)00272-5","volume":"138","author":"TP Hong","year":"2003","unstructured":"Hong TP, Liu KY, Wang SL (2003) Fuzzy data mining for interesting generalized association rules. Fuzzy sets and systems 138: 255\u2013269","journal-title":"Fuzzy sets and systems"},{"issue":"3","key":"21_CR21","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1016\/j.fss.2005.05.036","volume":"156","author":"E H\u00fcllermeier","year":"2005","unstructured":"H\u00fcllermeier E (2005) Fuzzy methods in machine learning and data mining: Status and prospects. Fuzzy Sets and Systems 156 (3): 387\u2013407","journal-title":"Fuzzy Sets and Systems"},{"key":"21_CR22","volume-title":"Classification and modeling with linguistic information granules","author":"H Ishibuchi","year":"2004","unstructured":"Ishibuchi H, Nakashima T, Nii M (2004) Classification and modeling with linguistic information granules Springer-Verlag, New York"},{"key":"21_CR23","unstructured":"Kavsek B, Lavrac N, Jovanoski V (2003) APRIORI-SD: Adapting association rule learning to subgroup discovery. In: Proceedings of the 5th International Symposium on Intelligent Data Analysis (IDA 2003). Berlin, pp. 230\u2013241"},{"key":"21_CR24","first-page":"249","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"W Kl\u00f6sgen","year":"1996","unstructured":"Kl\u00f6sgen W (1996) Explora: A Multipattern and Multistrategy Discovery Assistant. In Fayyad U, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in Knowledge Discovery and Data Mining, AAAI Press, California, pp. 249\u2013271"},{"key":"21_CR25","first-page":"354","volume-title":"Handbook of Data Mining and Knowledge Discovery","author":"W Kl\u00f6sgen","year":"2002","unstructured":"Kl\u00f6sgen W (2002) Subgroup Discovery. In Kl\u00f6sgen W, Zytkow J (eds) Handbook of Data Mining and Knowledge Discovery. Oxford University Press, New York, pp. 354\u2013364"},{"key":"21_CR26","unstructured":"Kl\u00f6sgen W, May M (2002) Census Data Mining - An Application. In: 13th European Conference on Machine Learning (ECML\u201802) \/ 6th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD\u201802) workshop on on Mining Official Data. Helsinki, pp. 65\u201379"},{"key":"21_CR27","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1145\/273244.273257","volume":"27","author":"C Kuok","year":"1998","unstructured":"Kuok C, Fu A, Wong ML (1998) Mining fuzzy association rules in databases. ACM SIGMOD Record 27: 41\u201346.","journal-title":"ACM SIGMOD Record"},{"key":"21_CR28","doi-asserted-by":"crossref","unstructured":"Lavrac N, Flach P, Zupan B (1999) Rule evaluation measures: A unifying view. In: Proceedings of the 9th International Workshop on Inductive Logic Programming (ILP\u201899). Bled, Slovenia, pp. 174\u2013185","DOI":"10.1007\/3-540-48751-4_17"},{"key":"21_CR29","first-page":"153","volume":"5","author":"N Lavrac","year":"2004","unstructured":"Lavrac N, Kavsec B, Flach P, Todorovski L (2004) Subgroup discovery with CN2-SD. Journal of Machine Learning Research 5: 153\u2013188","journal-title":"Journal of Machine Learning Research"},{"key":"21_CR30","doi-asserted-by":"crossref","unstructured":"Lavrac N, Zelezny F, Flach P (2003) RSD: Relational subgroup discovery through first-order feature construction. In: Proceedings of the 13th International Conference on Inductive Logic Programming (ILP 2003). Szeged, Hungary, pp. 149\u2013165","DOI":"10.1007\/3-540-36468-4_10"},{"key":"21_CR31","unstructured":"Michie D, Spiegelhalter DJ, Taylor CC (1994) Machine learning, neural and estatistical classification. Ellis Horwood"},{"key":"21_CR32","unstructured":"Piatetsky-Shapiro G, Matheus, C (1994) The interestingness of deviation. In: Proceedings of the AAAI-94 Workshop on Knowledge Discovery in Databases. Seattle, Washington, pp. 25\u201336"},{"key":"21_CR33","unstructured":"Quinlan JR (1987) Generating Production Rules from Decision Trees. In: Proceedings of the 10th International Joint Conference on Artificial Intelligence (IJCAI\u201887). Milan, Italy, pp. 304\u2013307"},{"key":"21_CR34","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1023\/A:1007361123060","volume":"26","author":"LD Raedt","year":"1997","unstructured":"Raedt LD, Dehaspe L (1997) Clausal discovery. Machine Learning 26: 99\u2013146","journal-title":"Machine Learning"},{"key":"21_CR35","doi-asserted-by":"crossref","unstructured":"Wrobel S (1997) An algorithm for multi-relational discovery of subgroups. In Proceedings of the First European Symposion on Principles of Data Mining and Knowledge Discovery (PKDD-97). Trondheim, Norway, pp. 78\u201387","DOI":"10.1007\/3-540-63223-9_108"},{"key":"21_CR36","doi-asserted-by":"publisher","first-page":"338","DOI":"10.1016\/S0019-9958(65)90241-X","volume":"8","author":"LA Zadeh","year":"1965","unstructured":"Zadeh LA (1965) Fuzzy sets. Information Control 8: 338\u2013353","journal-title":"Information Control"},{"key":"21_CR37","doi-asserted-by":"crossref","unstructured":"Zadeh LA (1975) The concept of a linguistic variable and its applications to approximate reasoning, parts I, II, III. Information Sciences 8\u20139: 199\u2013249, 301\u2013357, 43\u201380","DOI":"10.1016\/0020-0255(75)90017-1"},{"key":"21_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2003.09.017","volume":"164","author":"S Zhang","year":"2004","unstructured":"Zhang S, Lu J, Zhang C (2004) A fuzzy logic based method to acquire user threshold of minimum-support for mining association rules. Information Sciences 164: 1\u201316","journal-title":"Information Sciences"}],"container-title":["Studies in Fuzziness and Soft Computing","Fuzzy Sets and Their Extensions: Representation, Aggregation and Models"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-540-73723-0_21.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,27]],"date-time":"2021-04-27T09:56:57Z","timestamp":1619517417000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-540-73723-0_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[null]]},"ISBN":["9783540737223"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-540-73723-0_21","relation":{},"subject":[]}}