{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:04:47Z","timestamp":1740096287051,"version":"3.37.3"},"publisher-location":"Berlin, Heidelberg","reference-count":14,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783642408458"},{"type":"electronic","value":"9783642408465"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013]]},"DOI":"10.1007\/978-3-642-40846-5_58","type":"book-chapter","created":{"date-parts":[[2013,8,14]],"date-time":"2013-08-14T11:08:26Z","timestamp":1376478506000},"page":"578-587","source":"Crossref","is-referenced-by-count":1,"title":["A Sensitivity Analysis for Quality Measures of Quantitative Association Rules"],"prefix":"10.1007","author":[{"given":"Mar\u00eda","family":"Mart\u00ednez-Ballesteros","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Francisco","family":"Mart\u00ednez-\u00c1lvarez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alicia","family":"Troncoso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 C.","family":"Riquelme","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"58_CR1","unstructured":"Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the International Conference on Very Large Databases, pp. 478\u2013499 (1994)"},{"issue":"3","key":"58_CR2","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1007\/s00500-005-0476-x","volume":"10","author":"B. Alatas","year":"2006","unstructured":"Alatas, B., Akin, E.: An efficient genetic algorithm for automated mining of both positive and negative quantitative association rules. Soft Computing\u00a010(3), 230\u2013237 (2006)","journal-title":"Soft Computing"},{"issue":"1","key":"58_CR3","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1016\/j.asoc.2007.05.003","volume":"8","author":"B. Alatas","year":"2008","unstructured":"Alatas, B., Akin, E., Karci, A.: MODENAR: Multi-objective differential evolution algorithm for mining numeric association rules. Applied Soft Computing\u00a08(1), 646\u2013656 (2008)","journal-title":"Applied Soft Computing"},{"issue":"3","key":"58_CR4","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1007\/s00500-008-0323-y","volume":"13","author":"J. Alcal\u00e1-Fdez","year":"2009","unstructured":"Alcal\u00e1-Fdez, J., S\u00e1nchez, L., Garc\u00eda, S., del Jesus, M.J., Ventura, S., Garrell, J.M., Otero, J., Romero, C., Bacardit, J., Rivas, V.M., Fern\u00e1ndez, J.C., Herrera, F.: Keel: A software tool to assess evolutionary algorithms for data mining problems. Soft Computing\u00a013(3), 307\u2013318 (2009)","journal-title":"Soft Computing"},{"key":"58_CR5","series-title":"Lecture Notes in Artificial Intelligence","doi-asserted-by":"publisher","first-page":"573","DOI":"10.1007\/978-3-540-25929-9_70","volume-title":"Rough Sets and Current Trends in Computing","author":"D. Li","year":"2004","unstructured":"Li, D., Deogun, J., Spaulding, W., Shuart, B.: Towards missing data imputation: A study of fuzzy K-means clustering method. In: Tsumoto, S., S\u0142owi\u0144ski, R., Komorowski, J., Grzyma\u0142a-Busse, J.W. (eds.) RSCTC 2004. LNCS (LNAI), vol.\u00a03066, pp. 573\u2013579. Springer, Heidelberg (2004)"},{"key":"58_CR6","unstructured":"Guvenir, H.A., Uysal, I.: Bilkent university function approximation repository (2000), \n                    \n                      http:\/\/funapp.cs.bilkent.edu.tr"},{"key":"58_CR7","doi-asserted-by":"crossref","unstructured":"Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Empirical analysis of using weighted sum fitness functions in NSGA-II for many-objective 0\/1 knapsack problems. In: Proceedings of the International Conference on Computer Modelling and Simulation, pp. 71\u201376 (2009)","DOI":"10.1109\/UKSIM.2009.54"},{"issue":"10","key":"58_CR8","doi-asserted-by":"publisher","first-page":"2065","DOI":"10.1007\/s00500-011-0705-4","volume":"15","author":"M. Mart\u00ednez-Ballesteros","year":"2011","unstructured":"Mart\u00ednez-Ballesteros, M., Mart\u00ednez-\u00c1lvarez, F., Troncoso, A., Riquelme, J.C.: An evolutionary algorithm to discover quantitative association rules in multidimensional time series. Soft Computing\u00a015(10), 2065\u20132084 (2011)","journal-title":"Soft Computing"},{"key":"58_CR9","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Ballesteros, M., Mart\u00ednez-\u00c1lvarez, F., Troncoso, A., Riquelme, J.C.: Selecting the best measures to discover quantitative association rules. Neurocomputing (in press, 2013), doi: \n                    \n                      http:\/\/dx.doi.org\/10.1016\/j.neucom.2013.01.056","DOI":"10.1016\/j.neucom.2013.01.056"},{"key":"58_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1007\/978-3-642-21222-2_39","volume-title":"Hybrid Artificial Intelligent Systems","author":"M. Mart\u00ednez-Ballesteros","year":"2011","unstructured":"Mart\u00ednez-Ballesteros, M., Riquelme, J.C.: Analysis of measures of quantitative association rules. In: Corchado, E., Kurzy\u0144ski, M., Wo\u017aniak, M. (eds.) HAIS 2011, Part II. LNCS, vol.\u00a06679, pp. 319\u2013326. Springer, Heidelberg (2011)"},{"key":"58_CR11","doi-asserted-by":"crossref","unstructured":"Mata, J., \u00c1lvarez, J., Riquelme, J.C.: Mining numeric association rules with genetic algorithms. In: Proceedings of the International Conference on Adaptive and Natural Computing Algorithms, pp. 264\u2013267 (2001)","DOI":"10.1007\/978-3-7091-6230-9_65"},{"issue":"1","key":"58_CR12","doi-asserted-by":"publisher","first-page":"585","DOI":"10.1016\/j.eswa.2011.07.049","volume":"39","author":"V. Pach\u00f3n \u00c1lvarez","year":"2012","unstructured":"Pach\u00f3n \u00c1lvarez, V., Mata V\u00e1zquez, J.: An evolutionary algorithm to discover quantitative association rules from huge databases without the need for an a priori discretization. Expert Systems with Applications\u00a039(1), 585\u2013593 (2012)","journal-title":"Expert Systems with Applications"},{"key":"58_CR13","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.ins.2012.07.001","volume":"218","author":"R. Pears","year":"2013","unstructured":"Pears, R., Koh, Y.S., Dobbie, G., Yeap, W.: Weighted association rule mining via a graph based connectivity model. Information Sciences\u00a0218, 61\u201384 (2013)","journal-title":"Information Sciences"},{"key":"58_CR14","doi-asserted-by":"crossref","unstructured":"Soto, W., Olaya-Benavides, A.: A genetic algorithm for discovery of association rules. In: Proceedings of the International Conference of the Chilean Computer Science Society, pp. 289\u2013293 (2011)","DOI":"10.1109\/SCCC.2011.37"}],"container-title":["Lecture Notes in Computer Science","Hybrid Artificial Intelligent Systems"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-40846-5_58","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T12:30:12Z","timestamp":1558009812000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-642-40846-5_58"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013]]},"ISBN":["9783642408458","9783642408465"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-642-40846-5_58","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2013]]}}}