{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T04:29:21Z","timestamp":1759206561338},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2015,4,1]],"date-time":"2015-04-01T00:00:00Z","timestamp":1427846400000},"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":["New Gener. Comput."],"published-print":{"date-parts":[[2015,4]]},"DOI":"10.1007\/s00354-015-0202-x","type":"journal-article","created":{"date-parts":[[2015,4,12]],"date-time":"2015-04-12T00:24:46Z","timestamp":1428798286000},"page":"115-135","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Data Mining: From Procedural to Declarative Approaches"],"prefix":"10.1007","volume":"33","author":[{"given":"Hendrik","family":"Blockeel","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2015,4,13]]},"reference":[{"key":"202_CR1","doi-asserted-by":"crossref","unstructured":"Adam, A., Blockeel, H., Govers, S. and Aertsen, A., \u201cSCCQL: A constraint-based clustering system,\u201d in Lecture Notes in Computer Science, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD), Prague, 23\u201327 September, 2013, Springer, pp. 681\u2013684, September 2013.","DOI":"10.1007\/978-3-642-40994-3_54"},{"key":"202_CR2","unstructured":"Agrawal, R. and Srikant, R., \u201cFast algorithms for mining association rules in large databases\u201d in VLDB\u201994, Proceedings of 20th International Conference on Very Large Data Bases, September 12\u201315, 1994, Santiago de Chile, Chile, Morgan Kaufmann, pp. 487\u2013499, 1994."},{"key":"202_CR3","unstructured":"Andrews, T., Blockeel, H., Bogaerts, B., Bruynooghe, M., Denecker, M., De Pooter, S., Mac\u00e9, C. and Ramon, J., \u201cAnalyzing manuscript traditions using constraint-based data mining,\u201d in Proceedings First Workshop on Combining Constraint Solving with Mining and Learning (ECAI 2012 Workshop), First Workshop on Combining Constraint Solving with Mining and Learning, Montpellier, France, 27 August 2012, pp. 15\u201320, August 2012."},{"key":"202_CR4","unstructured":"Bar-Hillel, A., Hertz, T., Shental, N. and Weinshall, D., \u201cLearning a mahalanobis metric from equivalence constraints,\u201dJournal of Machine Learning Research, 6, pp. 937\u2013965, 2005."},{"key":"202_CR5","unstructured":"Bengio, Y. and Grandvalet, Y., \u201cNo unbiased estimator of the variance of k-fold cross-validation,\u201d Journal of Machine Learning Research, 5, pp. 1089\u20131105, 2004."},{"key":"202_CR6","doi-asserted-by":"crossref","unstructured":"Blockeel, H., Calders, T., Fromont, \u00c9., Goethals, B., Prado, A. and Robardet, C., \u201cAn inductive database system based on virtual mining views,\u201d Data Min. Knowl. Discov., 24(1), pp. 247\u2013287, 2012.","DOI":"10.1007\/s10618-011-0229-7"},{"key":"202_CR7","unstructured":"Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J., Classification and Regression Trees, Wadsworth, 1984."},{"key":"202_CR8","doi-asserted-by":"crossref","unstructured":"Bruynooghe, M., Blockeel, H., Bogaerts, B., De Cat, B., De Pooter, S., Jansen, J., Labarre, A., Ramon, J., Denecker, M. and Verwer, S., \u201cPredicate logic as a modeling language: Modeling and solving some machine learning and data mining problems with IDP3,\u201d Theory and Practice of Logic Programming, available on CJO2014, doi: 10.1017\/S147106841400009X .","DOI":"10.1017\/S147106841400009X"},{"key":"202_CR9","doi-asserted-by":"crossref","unstructured":"Dao, T., Duong, K. and Vrain, C., \u201cA declarative framework for constrained clustering,\u201d in Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23\u201327, 2013, Proceedings, Part III, pp. 419\u2013434, 2013.","DOI":"10.1007\/978-3-642-40994-3_27"},{"key":"202_CR10","doi-asserted-by":"crossref","unstructured":"Davidson, I., \u201cClustering with constraints,\u201d in Encyclopedia of Database Systems, Springer, pp. 393\u2013396, 2009.","DOI":"10.1007\/978-0-387-39940-9_610"},{"key":"202_CR11","unstructured":"De Pooter, S., Wittocx, J. and Denecker. M., \u201cA prototype of a knowledge-based programming environment,\u201d in Proceedings of the 19th International Conference on Applications of Declarative Programming and Knowledge Management (INAP 2011), Vienna, 28\u201330 September 2011, pp. 6, August 2011."},{"key":"202_CR12","unstructured":"Demsar, J., \u201cStatistical comparisons of classi_ers over multiple data sets\u201d Journal of Machine Learning Research, 7, pp. 1\u201330, 2006."},{"key":"202_CR13","doi-asserted-by":"crossref","unstructured":"Dietterich T. G. \u201cApproximate statistical test for comparing supervised classification learning algorithms,\u201d Neural Computation, 10(7), pp. 1895\u20131923, 1998.","DOI":"10.1162\/089976698300017197"},{"key":"202_CR14","doi-asserted-by":"crossref","unstructured":"Domingos, P. and Hulten, G., \u201cMining high-speed data streams,\u201d in Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, Boston, MA, USA, August 20\u201323, 2000 (Ramakrishnan, R., Stolfo, S J., Bayardo, R. J., and Parsa, I., eds.), ACM, pp. 71\u201380, 2000.","DOI":"10.1145\/347090.347107"},{"key":"202_CR15","doi-asserted-by":"crossref","unstructured":"D\u017eeroski, S., Goethals, B. and Panov, P., eds., Inductive Databases and Constraint-Based Data Mining, Springer, 2010.","DOI":"10.1007\/978-1-4419-7738-0"},{"key":"202_CR16","unstructured":"Frawley, W. J., Piatetsky-shapiro, G. and Matheus, C. J., \u201cKnowledge discovery in databases: an overview,\u201d AI Magazine, 13, 1992."},{"key":"202_CR17","doi-asserted-by":"crossref","unstructured":"Garofalakis, M. N., Rastogi, R. and Shim, K., \u201cMining sequential patterns with regular expression constraints,\u201d IEEE Trans. Knowl. Data Eng., 14(3), pp. 530\u2013552, 2002.","DOI":"10.1109\/TKDE.2002.1000341"},{"key":"202_CR18","doi-asserted-by":"crossref","unstructured":"Guazzelli, A., Zeller, M., Lin, W. and Williams, G., \u201cPMML: An open standard for sharing models,\u201d The R Journal, 1(1), pp. 60\u201365, 2009.","DOI":"10.32614\/RJ-2009-010"},{"key":"202_CR19","unstructured":"Guns, T., Dries, A., Tack, G., Nijssen, S. and De Raedt, L., \u201cMiningzinc: A modeling language for constraint-based mining,\u201d in IJCAI 2013, Proceedings of the 23rd International Joint Conference on Artificial Intelligence, Beijing, China, August 3\u20139, 2013, pp. 1365\u20131372, 2013."},{"key":"202_CR20","doi-asserted-by":"crossref","unstructured":"Hu, P., Vens, C., Verstrynge, B. and Blockeel, H., \u201cGeneralizing from example clusters,\u201d in Lecture Notes in Computer Science, Discovery Science, Singapore, 6\u20139 October 2013, Springer, pp. 64\u201378, October 2013.","DOI":"10.1007\/978-3-642-40897-7_5"},{"key":"202_CR21","doi-asserted-by":"crossref","unstructured":"Imielinski T. and Mannila, H., \u201cA database perspective on knowledge discovery,\u201d Commun. ACM, 39(11), pp. 58\u201364, 1996.","DOI":"10.1145\/240455.240472"},{"key":"202_CR22","unstructured":"Meo, R., Psaila, G. and Ceri, S., \u201cA new sql-like operator for mining association rules,\u201d in VLDB\u201996, Proceedings of 22th International Conference on Very Large Data Bases, September 3\u20136, 1996, Mumbai (Bombay), India, Morgan Kaufmann, pp. 122\u2013133, 1996."},{"key":"202_CR23","doi-asserted-by":"crossref","unstructured":"Muggleton, S., \u201cInductive logic programming,\u201d New Generation Comput., 8(4), pp. 295\u2013318, 1991.","DOI":"10.1007\/BF03037089"},{"key":"202_CR24","doi-asserted-by":"crossref","unstructured":"Muggleton, S. and De Raedt, L., \u201cInductive logic programming: Theory and methods,\u201d J. Log. Program., 19\/20, pp. 629\u2013679, 1994.","DOI":"10.1016\/0743-1066(94)90035-3"},{"key":"202_CR25","doi-asserted-by":"crossref","unstructured":"Nijssen, S. and Guns, T., \u201cIntegrating constraint programming and itemset mining,\u201d in Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2010, Barcelona, Spain, September 20\u201324, 2010, Proceedings, Part II, pp. 467\u2013482, 2010.","DOI":"10.1007\/978-3-642-15883-4_30"},{"key":"202_CR26","doi-asserted-by":"crossref","unstructured":"Quinlan, J. R., \u201cInduction of decision trees,\u201d Machine Learning, 1(1), pp. 81\u2013106, 1986.","DOI":"10.1007\/BF00116251"},{"key":"202_CR27","unstructured":"Quinlan, J. R., C4.5: Programs for Machine Learning, Morgan Kaufmann, 1993."},{"key":"202_CR28","doi-asserted-by":"crossref","unstructured":"De Raedt, L., Logical and relational learning, Cognitive Technologies, Springer, 2008.","DOI":"10.1007\/978-3-540-68856-3"},{"key":"202_CR29","unstructured":"Tsochantaridis, I., Joachims, T., Hofmann, T. and Altun, Y., \u201cLarge margin methods for structured and interdependent output variables,\u201d Journal of Machine Learning Research, pp. 1453\u20131484, 2005."},{"key":"202_CR30","doi-asserted-by":"crossref","unstructured":"Tsoumakas, G., Katakis, I. and Vlahavas, I. P., \u201cMining multilabel data\u201d in Data Mining and Knowledge Discovery Handbook, 2nd ed., Springer, pp. 667\u2013685, 2010.","DOI":"10.1007\/978-0-387-09823-4_34"},{"key":"202_CR31","unstructured":"Vanwinckelen, G. and Blockeel, H., \u201cA declarative query language for statistical inference\u201d ECML\/PKDD 2013 Workshop: Languages for Data Mining and Machine Learning, Prague, Czech Republic, 23 September 2013, September 2013."},{"key":"202_CR32","unstructured":"Wagstaff K., Cardie, C., Rogers, S. and Schr\u00f6dl, S., \u201cConstrained k-means clustering with background knowledge,\u201d in Proceedings of the Eighteenth International Conference on Machine Learning (ICML 2001), Williams College, Williamstown, MA, USA, June 28 - July 1, 2001, Morgan Kaufmann, pp. 577\u2013584, 2001."},{"key":"202_CR33","doi-asserted-by":"crossref","unstructured":"Wang, Y., Ramon, J. and Fannes, T., \u201cAn effciently computable subgraph pattern support measure: counting independent observations, Data Min. Knowl. Discov., 27(3), pp. 444\u2013477, 2013.","DOI":"10.1007\/s10618-013-0318-x"},{"key":"202_CR34","unstructured":"Zhi, W., Wang, X., Qian, B., Butler, P., Ramakrishnan, N. and Davidson, I., \u201cClustering with complex constraints - algorithms and applications,\u201d in Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence, July 14\u201318, 2013 (desJardins, M. and Littman, M. L., eds.), Bellevue, Washington, USA. AAAI Press, 2013."}],"container-title":["New Generation Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00354-015-0202-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00354-015-0202-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00354-015-0202-x","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T20:11:54Z","timestamp":1598904714000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00354-015-0202-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,4]]},"references-count":34,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2015,4]]}},"alternative-id":["202"],"URL":"https:\/\/doi.org\/10.1007\/s00354-015-0202-x","relation":{},"ISSN":["0288-3635","1882-7055"],"issn-type":[{"value":"0288-3635","type":"print"},{"value":"1882-7055","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,4]]}}}