{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,2]],"date-time":"2025-12-02T15:19:14Z","timestamp":1764688754191,"version":"3.40.3"},"publisher-location":"Berlin, Heidelberg","reference-count":22,"publisher":"Springer Berlin Heidelberg","isbn-type":[{"type":"print","value":"9783642042768"},{"type":"electronic","value":"9783642042775"}],"license":[{"start":{"date-parts":[[2009,1,1]],"date-time":"2009-01-01T00:00:00Z","timestamp":1230768000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2009]]},"DOI":"10.1007\/978-3-642-04277-5_3","type":"book-chapter","created":{"date-parts":[[2009,10,1]],"date-time":"2009-10-01T08:49:52Z","timestamp":1254386992000},"page":"20-29","source":"Crossref","is-referenced-by-count":13,"title":["Mining Rules for the Automatic Selection Process of Clustering Methods Applied to Cancer Gene Expression Data"],"prefix":"10.1007","author":[{"given":"Andr\u00e9 C. A.","family":"Nascimento","sequence":"first","affiliation":[]},{"given":"Ricardo B. C.","family":"Prud\u00eancio","sequence":"additional","affiliation":[]},{"given":"Marcilio C. P.","family":"de Souto","sequence":"additional","affiliation":[]},{"given":"Ivan G.","family":"Costa","sequence":"additional","affiliation":[]}],"member":"297","reference":[{"issue":"5439","key":"3_CR1","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1126\/science.286.5439.531","volume":"286","author":"T. Golub","year":"1999","unstructured":"Golub, T., et al.: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science\u00a0286(5439), 531\u2013537 (1999)","journal-title":"Science"},{"issue":"6769","key":"3_CR2","doi-asserted-by":"publisher","first-page":"503","DOI":"10.1038\/35000501","volume":"403","author":"A.A. Alizadeh","year":"2000","unstructured":"Alizadeh, A.A., et al.: Distinct types of diffuse large b-cell lymphoma identified by gene expression profiling. Nature\u00a0403(6769), 503\u2013511 (2000)","journal-title":"Nature"},{"issue":"2","key":"3_CR3","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/S1478-5382(03)02329-1","volume":"1","author":"R. Spang","year":"2003","unstructured":"Spang, R.: Diagnostic signatures from microarrays: a bioinformatics concept for personalized medicine. Biosilico\u00a01(2), 64\u201368 (2003)","journal-title":"Biosilico"},{"issue":"4","key":"3_CR4","doi-asserted-by":"publisher","first-page":"623","DOI":"10.1590\/S1415-47572004000400025","volume":"27","author":"I.G. Costa","year":"2004","unstructured":"Costa, I.G., et al.: Comparative analysis of clustering methods for gene expression time course data. Genetics and Molecular Biology\u00a027(4), 623\u2013631 (2004)","journal-title":"Genetics and Molecular Biology"},{"key":"3_CR5","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1186\/1471-2105-7-397","volume":"7","author":"S. Datta","year":"2006","unstructured":"Datta, S., Datta, S.: Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes. BMC Bioinformatics\u00a07, 397 (2006)","journal-title":"BMC Bioinformatics"},{"issue":"12","key":"3_CR6","doi-asserted-by":"publisher","first-page":"1499","DOI":"10.1038\/nbt1205-1499","volume":"23","author":"P. D\u2019haeseleer","year":"2005","unstructured":"D\u2019haeseleer, P.: How does gene expression clustering work? Nature Biotechnology\u00a023(12), 1499\u20131501 (2005)","journal-title":"Nature Biotechnology"},{"key":"3_CR7","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1186\/1471-2105-9-497","volume":"9","author":"M.C. Souto de","year":"2008","unstructured":"de Souto, M.C., et al.: Clustering cancer gene expression data: a comparative study. BMC Bioinformatics\u00a09, 497 (2008)","journal-title":"BMC Bioinformatics"},{"issue":"31","key":"3_CR8","first-page":"31","volume":"1","author":"R. Vilalta","year":"2004","unstructured":"Vilalta, R., et al.: Using meta-learning to support data- mining. Intern. Journal of Computer Science Application\u00a01(31), 31\u201345 (2004)","journal-title":"Intern. Journal of Computer Science Application"},{"issue":"3","key":"3_CR9","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1023\/B:MACH.0000015878.60765.42","volume":"54","author":"C. Giraud-Carrier","year":"2004","unstructured":"Giraud-Carrier, C., et al.: Introduction to the special issue on meta-learning. Machine Learning\u00a054(3), 187\u2013193 (2004)","journal-title":"Machine Learning"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Smith-Miles, K.: Towards insightful algorithm selection for optimisation using meta-learning concepts. In: Proceedings of the IEEE International Joint Conference on Neural Networks 2008, pp. 4118\u20134124 (2008)","DOI":"10.1109\/IJCNN.2008.4634391"},{"issue":"3","key":"3_CR11","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1023\/A:1021713901879","volume":"50","author":"P. Brazdil","year":"2003","unstructured":"Brazdil, P., et al.: Ranking learning algorithms: Using IBL and meta-learning on accuracy and time results. Machine Learning\u00a050(3), 251\u2013277 (2003)","journal-title":"Machine Learning"},{"issue":"3","key":"3_CR12","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1023\/B:MACH.0000015882.38031.85","volume":"54","author":"A. Kalousis","year":"2004","unstructured":"Kalousis, A., Gama, J., Hilario, M.: On data and algorithms - understanding inductive performance. Machine Learning\u00a054(3), 275\u2013312 (2004)","journal-title":"Machine Learning"},{"key":"3_CR13","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1016\/j.neucom.2004.03.008","volume":"61","author":"R.B.C. Prud\u011bncio","year":"2004","unstructured":"Prud\u011bncio, R.B.C., Ludermir, T.B.: Meta-learning approaches to selecting time series models. Neurocomputing\u00a061, 121\u2013137 (2004)","journal-title":"Neurocomputing"},{"key":"3_CR14","unstructured":"Wang, X., et al.: Rule induction for forecasting method selection: Meta-learning the characteristics of univariate time series. Neurocomputing (2008) (to appear)"},{"key":"3_CR15","unstructured":"Tsoumakas, G., et al.: Lazy adaptive multicriteria planning. In: Proceedings of the 16th European Conference on Artificial Intelligence, ECAI 2004, pp. 693\u2013697 (2004)"},{"issue":"1","key":"3_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/1456650.1456656","volume":"41","author":"K. Smith-Miles","year":"2008","unstructured":"Smith-Miles, K.: Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Computing Surveys\u00a041(1), 1\u201325 (2008)","journal-title":"ACM Computing Surveys"},{"key":"3_CR17","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/BF01897163","volume":"5","author":"G. Milligan","year":"1988","unstructured":"Milligan, G., Cooper, M.: A study of standardization of variables in cluster analysis. Journal of Classification\u00a05, 181\u2013204 (1988)","journal-title":"Journal of Classification"},{"key":"3_CR18","volume-title":"Proceedings of the International Joint Conference on Neural Networks","author":"M.C.P. Souto de","year":"2008","unstructured":"de Souto, M.C.P., et al.: Ranking and selecting clustering algorithms using a meta-learning approach. In: Proceedings of the International Joint Conference on Neural Networks. IEEE Computer Society, Los Alamitos (2008)"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Wilson, D.L.: Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man and Cybernetics\u00a0(3) (1972)","DOI":"10.1109\/TSMC.1972.4309137"},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Dembczy\u0144ski, K., Kot\u0142owski, W., S\u0142owi\u0144ski, R.: Maximum likelihood rule ensembles. In: Proceedings of the 25th International Conference on Machine Learning, ICML, pp. 224\u2013231 (2008)","DOI":"10.1145\/1390156.1390185"},{"key":"3_CR21","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L. Breiman","year":"2001","unstructured":"Breiman, L.: Random forests. Machine Learning\u00a045, 5\u201332 (2001)","journal-title":"Machine Learning"},{"key":"3_CR22","volume-title":"Microarray Gene Expression Data Analysis: A Beginner\u2019s Guide","author":"H.C. Cauton","year":"2003","unstructured":"Cauton, H.C., Quackenbush, J., Brazma, A.: Microarray Gene Expression Data Analysis: A Beginner\u2019s Guide. Blackwell Publishing, Malden (2003)"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks \u2013 ICANN 2009"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-642-04277-5_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,22]],"date-time":"2019-05-22T19:56:19Z","timestamp":1558554979000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-642-04277-5_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2009]]},"ISBN":["9783642042768","9783642042775"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-642-04277-5_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2009]]}}}