{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T17:07:38Z","timestamp":1721840858459},"reference-count":75,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2016,3,1]],"date-time":"2016-03-01T00:00:00Z","timestamp":1456790400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100003593","name":"CNPq","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003593","id-type":"DOI","asserted-by":"crossref"}]},{"name":"FACEPE (Brazilian Agencies)"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Pattern Recognition"],"published-print":{"date-parts":[[2016,3]]},"DOI":"10.1016\/j.patcog.2015.09.025","type":"journal-article","created":{"date-parts":[[2015,10,2]],"date-time":"2015-10-02T04:03:03Z","timestamp":1443758583000},"page":"310-321","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":20,"special_numbering":"C","title":["Kernel-based hard clustering methods with kernelization of the metric and automatic weighting of the variables"],"prefix":"10.1016","volume":"51","author":[{"given":"Marcelo R.P.","family":"Ferreira","sequence":"first","affiliation":[]},{"given":"Francisco de A.T.","family":"de Carvalho","sequence":"additional","affiliation":[]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1224-8508","authenticated-orcid":false,"given":"Eduardo C.","family":"Sim\u00f5es","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.patcog.2015.09.025_bib1","unstructured":"S. Aeberhard, D. Coomans, O. de Vel, Comparison of Classifiers in High Dimensional Settings, Technical Report, Dept. of Computer Science and Dept. of Mathematics and Statistics, James Cook University of North Queensland, 1992."},{"key":"10.1016\/j.patcog.2015.09.025_bib2","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1016\/j.patcog.2011.08.012","article-title":"Minkowski metric, feature weighting and anomalous cluster initializing in k-means clustering","volume":"45","author":"de Amorim","year":"2012","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2015.09.025_bib3","unstructured":"K. Bache, M. Lichman, UCI Machine Learning Repository, 2013. url: \u3008http:\/\/archive.ics.uci.edu\/ml\u3009."},{"key":"10.1016\/j.patcog.2015.09.025_bib4","series-title":"Modern Information Retrieval","author":"Baeza-Yates","year":"1999"},{"key":"10.1016\/j.patcog.2015.09.025_bib5","first-page":"125","article-title":"Support vector clustering","volume":"2","author":"Ben-Hur","year":"2001","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.patcog.2015.09.025_bib6","series-title":"Pattern Recognition with Fuzzy Objective Function Algorithms","author":"Bezdek","year":"1981"},{"key":"10.1016\/j.patcog.2015.09.025_bib7","doi-asserted-by":"crossref","unstructured":"S. Borer, W. Gerstner, A new kernel clustering algorithm, in: Proceedings of the Ninth International Conference on Neural Information Processing, vol. 5, pp. 2527\u20132531.","DOI":"10.1109\/ICONIP.2002.1201950"},{"key":"10.1016\/j.patcog.2015.09.025_bib8","series-title":"Classification and Regression Trees","author":"Breiman","year":"1984"},{"key":"10.1016\/j.patcog.2015.09.025_bib9","first-page":"381","article-title":"Handwritten digit recognition by combined classifiers","volume":"34","author":"van Breukelen","year":"1998","journal-title":"Kybernetika"},{"key":"10.1016\/j.patcog.2015.09.025_bib10","doi-asserted-by":"crossref","unstructured":"F. Camastra, M. Spinetti, A. Vinciarelli, Offline cursive character challenge: a new benchmark for machine learning and pattern recognition algorithms, in: 18th International Conference on Pattern Recognition, vol. 2, ICPR 2006, 2006, pp. 913\u2013916.","DOI":"10.1109\/ICPR.2006.895"},{"key":"10.1016\/j.patcog.2015.09.025_bib11","first-page":"801","article-title":"A novel kernel method for clustering","volume":"27","author":"Camastra","year":"2005","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.patcog.2015.09.025_bib12","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1016\/S0167-8655(01)00008-3","article-title":"Cursive character recognition by learning vector quantization","volume":"22","author":"Camastra","year":"2001","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.patcog.2015.09.025_bib13","doi-asserted-by":"crossref","unstructured":"F. Camastra, A. Vinciarelli, Machine Learning for Audio, Image and Video Analysis - Theory and Applications, Advanced Information and Knowledge Processing, Springer, London, 2008. URL: http:\/\/dx.doi.org\/10.1007\/978-1-84800-007-0.","DOI":"10.1007\/978-1-84800-007-0"},{"key":"10.1016\/j.patcog.2015.09.025_bib14","unstructured":"B. Caputo, K. Sim, F. Furesjo, A. Smola, Appearance-based object recognition using svms: which kernel should I use? in: Proceedings of NIPS Workshop on Statistical Methods for Computational Experiments in Visual Processing and Computer Vision."},{"key":"10.1016\/j.patcog.2015.09.025_bib15","unstructured":"G. Celeux, E. Diday, G. Govaert, Y. Lechevallier, H. Ralambondrainy, Classification Automatique des Donn\u00e9es, Bordas, Paris, 1989."},{"key":"10.1016\/j.patcog.2015.09.025_bib16","doi-asserted-by":"crossref","first-page":"943","DOI":"10.1016\/j.patcog.2003.11.003","article-title":"An optimization algorithm for clustering using weighted dissimilarity measures","volume":"37","author":"Chan","year":"2004","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2015.09.025_bib17","doi-asserted-by":"crossref","first-page":"27:1","DOI":"10.1145\/1961189.1961199","article-title":"Libsvm: a library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"10.1016\/j.patcog.2015.09.025_bib18","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s00180-006-0260-0","article-title":"New clustering methods for interval data","volume":"21","author":"Chavent","year":"2006","journal-title":"Comput. Stat."},{"key":"10.1016\/j.patcog.2015.09.025_bib19","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1109\/TSMCB.2004.831165","article-title":"Robust image segmentation using fcm with spatial constraints based on new kernel-induced distance measure","volume":"34","author":"Chen","year":"2004","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"10.1016\/j.patcog.2015.09.025_bib20","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1109\/TFUZZ.2003.814839","article-title":"A new kernel-based fuzzy clustering approach","volume":"11","author":"Chiang","year":"2003","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"10.1016\/j.patcog.2015.09.025_bib21","first-page":"93","article-title":"Comparison of multivariate discriminant techniques for clinical data\u2014application to the thyroid functional state","author":"Coomans","year":"1983","journal-title":"Methods Inf. Med."},{"key":"10.1016\/j.patcog.2015.09.025_bib22","doi-asserted-by":"crossref","first-page":"2833","DOI":"10.1016\/j.fss.2006.06.004","article-title":"Partitional fuzzy clustering methods based on adaptive quadratic distances","volume":"157","author":"de Carvalho","year":"2006","journal-title":"Fuzzy Sets Syst."},{"key":"10.1016\/j.patcog.2015.09.025_bib23","unstructured":"I.S. Dhillon, Y. Guan, B. Kulis, Kernel k-means spectral clustering and normalized cuts, in: Proceedings of 10th ACM International Conference on Knowledge Discovery and Data Mining, pp. 551\u2013556."},{"key":"10.1016\/j.patcog.2015.09.025_bib24","first-page":"329","article-title":"Classification automatique avec distances adaptatives","volume":"11","author":"Diday","year":"1977","journal-title":"R.A.I.R.O. Inf. Comput. Sci."},{"key":"10.1016\/j.patcog.2015.09.025_bib25","doi-asserted-by":"crossref","first-page":"3082","DOI":"10.1016\/j.patcog.2014.03.026","article-title":"Kernel-based hard clustering methods in the feature space with automatic variable weighting","volume":"47","author":"Ferreira","year":"2014","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2015.09.025_bib26","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.patcog.2007.05.018","article-title":"A survey of kernel and spectral methods for clustering","volume":"41","author":"Filippone","year":"2008","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2015.09.025_bib27","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","article-title":"The use of multiple measurements in taxonomic problems","author":"Fisher","year":"1936","journal-title":"Annu. Eugen."},{"key":"10.1016\/j.patcog.2015.09.025_bib28","doi-asserted-by":"crossref","unstructured":"A. Freire, G. Barreto, M. Veloso, A. Varela, Short-term memory mechanisms in neural network learning of robot navigation tasks: a case study, in: Robotics Symposium (LARS), 2009 6th Latin American, pp. 1\u20136.","DOI":"10.1109\/LARS.2009.5418323"},{"key":"10.1016\/j.patcog.2015.09.025_bib29","doi-asserted-by":"crossref","first-page":"780","DOI":"10.1109\/TNN.2002.1000150","article-title":"Mercer kernel-based clustering in feature space","volume":"13","author":"Girolami","year":"2002","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.patcog.2015.09.025_bib30","series-title":"Classification","author":"Gordon","year":"1999"},{"key":"10.1016\/j.patcog.2015.09.025_bib31","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/j.fss.2009.10.021","article-title":"Kernel-based fuzzy clustering and fuzzy clustering","volume":"161","author":"Graves","year":"2010","journal-title":"Fuzzy Sets Syst."},{"key":"10.1016\/j.patcog.2015.09.025_bib32","doi-asserted-by":"crossref","unstructured":"D.E. Gustafson, W.C. Kessel, Fuzzy clustering with a fuzzy covariance matrix, in: IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes, vol. 17, IEEE, 1978, pp. 761\u2013766.","DOI":"10.1109\/CDC.1978.268028"},{"key":"10.1016\/j.patcog.2015.09.025_bib33","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1109\/TNN.2005.860840","article-title":"Clustering via kernel decomposition","volume":"17","author":"Have","year":"2006","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.patcog.2015.09.025_bib34","series-title":"Fuzzy Cluster Analysis","author":"H\u00f6ppner","year":"1999"},{"key":"10.1016\/j.patcog.2015.09.025_bib35","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1109\/TPAMI.2005.95","article-title":"Automated variable weighting in k-means type clustering","volume":"27","author":"Huang","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2015.09.025_bib36","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF01908075","article-title":"Comparing partitions","volume":"2","author":"Hubert","year":"1985","journal-title":"J. Class."},{"key":"10.1016\/j.patcog.2015.09.025_bib37","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1109\/34.291440","article-title":"A database for handwritten text recognition research","volume":"16","author":"Hull","year":"1994","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.patcog.2015.09.025_bib38","doi-asserted-by":"crossref","unstructured":"R. Inokuchi, S. Miyamoto, Lvq clustering and som using a kernel function, in: Proceedings of IEEE International Conference on Fuzzy Systems, vol. 3, pp. 1497\u20131500.","DOI":"10.1109\/FUZZY.2004.1375395"},{"key":"10.1016\/j.patcog.2015.09.025_bib39","doi-asserted-by":"crossref","first-page":"651","DOI":"10.1016\/j.patrec.2009.09.011","article-title":"Data clustering","volume":"31","author":"Jain","year":"2010","journal-title":"Pattern Recogn. Lett."},{"key":"10.1016\/j.patcog.2015.09.025_bib40","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1145\/331499.331504","article-title":"Data clustering","volume":"31","author":"Jain","year":"1999","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.patcog.2015.09.025_bib41","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1109\/TKDE.2007.1048","article-title":"An entropy weighting k-means algorithm for subspace clustering of high-dimensional sparse data","volume":"19","author":"Jing","year":"2007","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.patcog.2015.09.025_bib42","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1007\/BF02520002","article-title":"Variability of impedivity in normal and pathological breast tissue","author":"Jossinet","year":"1996","journal-title":"Med. Biol. Eng. Comput."},{"key":"10.1016\/j.patcog.2015.09.025_bib43","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.knosys.2012.08.009","article-title":"Developing intuitive knowledge classifier and modeling of users\u05f3 domain dependent data in web","volume":"37","author":"Kahraman","year":"2013","journal-title":"Knowl. Based Syst."},{"key":"10.1016\/j.patcog.2015.09.025_bib44","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/j.patcog.2004.09.006","article-title":"Evaluation of the performance of clustering algorithms in kernel-induced feature space","volume":"38","author":"Kim","year":"2005","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.patcog.2015.09.025_bib45","doi-asserted-by":"crossref","first-page":"879","DOI":"10.1016\/j.patrec.2004.10.001","article-title":"A kernel-based subtractive clustering method","volume":"26","author":"Kim","year":"2005","journal-title":"Pattern Recognit. Lett."},{"key":"10.1016\/j.patcog.2015.09.025_bib46","doi-asserted-by":"crossref","first-page":"1146","DOI":"10.1109\/TKDE.2014.2359662","article-title":"Voronoi cell-based clustering using a kernel support","volume":"27","author":"Kim","year":"2015","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"10.1016\/j.patcog.2015.09.025_bib47","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1080\/08839519508945477","article-title":"Statlog","volume":"9","author":"King","year":"1995","journal-title":"Appl. Artif. Intell."},{"key":"10.1016\/j.patcog.2015.09.025_bib48","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1007\/BF00337288","article-title":"Self-organized formation of topologically correct feature maps","volume":"43","author":"Kohonen","year":"1982","journal-title":"Biol. Cybern."},{"key":"10.1016\/j.patcog.2015.09.025_bib49","doi-asserted-by":"crossref","unstructured":"T. Kohonen, The self-organizing map, in: Proceedings of the IEEE, vol. 78, pp. 1464\u20131480.","DOI":"10.1109\/5.58325"},{"key":"10.1016\/j.patcog.2015.09.025_bib50","series-title":"Self-Organizing Maps","author":"Kohonen","year":"2001"},{"key":"10.1016\/j.patcog.2015.09.025_bib51","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.neunet.2012.09.018","article-title":"Essentials of the self-organizing map","volume":"37","author":"Kohonen","year":"2013","journal-title":"Neural Netw."},{"key":"10.1016\/j.patcog.2015.09.025_bib52","doi-asserted-by":"crossref","unstructured":"K. Lang, NewsWeeder: learning to filter netnews, in: 12th International Conference on Machine Learning (ICML95), pp. 331\u2013339.","DOI":"10.1016\/B978-1-55860-377-6.50048-7"},{"key":"10.1016\/j.patcog.2015.09.025_bib53","doi-asserted-by":"crossref","unstructured":"Y. LeCun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, in: Proceedings of the IEEE, vol. 86, pp. 2278\u20132324.","DOI":"10.1109\/5.726791"},{"key":"10.1016\/j.patcog.2015.09.025_bib54","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s10994-009-5154-2","article-title":"Particle swarm optimizer for variable weighting in clustering high-dimensional data","volume":"82","author":"Lu","year":"2011","journal-title":"Mach. Learn."},{"key":"10.1016\/j.patcog.2015.09.025_bib55","unstructured":"D. Macdonald, C. Fyfe, The kernel self-organizing map, in: Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies, vol. 1, pp. 317\u2013320."},{"key":"10.1016\/j.patcog.2015.09.025_bib56","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1287\/opre.43.4.570","article-title":"Breast cancer diagnosis and prognosis via linear programming","volume":"43","author":"Mangasarian","year":"1995","journal-title":"Oper. Res."},{"key":"10.1016\/j.patcog.2015.09.025_bib57","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1109\/72.238311","article-title":"\u2018Neural gas\u2019 network for vector quantization and its application to time-series prediction","volume":"4","author":"Martinetz","year":"1993","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.patcog.2015.09.025_bib58","doi-asserted-by":"crossref","unstructured":"J. Mercer, Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations, Philosophical Transactions of the Royal Society of London, Series A, Containing Papers of a Mathematical or Physical Character, Vol. 209 (1909), pp. 415\u2013446.","DOI":"10.1098\/rsta.1909.0016"},{"key":"10.1016\/j.patcog.2015.09.025_bib59","series-title":"Clustering and Classification","first-page":"341","author":"Milligan","year":"1996"},{"key":"10.1016\/j.patcog.2015.09.025_bib60","doi-asserted-by":"crossref","unstructured":"B. Mokhlesabadifarahani, V. Gunjan, Methodology for working with emg dataset, in: EMG Signals Characterization in Three States of Contraction by Fuzzy Network and Feature Extraction, Springer Briefs in Applied Sciences and Technology, Springer, Singapore, 2015, pp. 11\u201320.","DOI":"10.1007\/978-981-287-320-0_2"},{"key":"10.1016\/j.patcog.2015.09.025_bib61","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1109\/72.914517","article-title":"An introduction to kernel-based learning algorithms","volume":"12","author":"M\u00fcller","year":"2001","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.patcog.2015.09.025_bib62","unstructured":"W.J. Nash, T.L. Sellers, S.R. Talbot, A.J. Cawthorn, W.B. Ford, The Population Biology of Abalone (Haliotis species) in Tasmania. I. Blacklip Abalone (H. rubra) from the North Coast and Islands of Bass Strait, Technical Report, Department of Computer Science, University of Tasmania, 1994."},{"key":"10.1016\/j.patcog.2015.09.025_bib63","unstructured":"A.K. Qinand, P.N. Suganthan, Kernel neural gas algorithms with application to cluster analysis, in: ICPR \u2013 17th International Conference on Pattern Recognition (ICPR\u05f304), vol. 4, pp. 617\u2013620."},{"key":"10.1016\/j.patcog.2015.09.025_bib64","first-page":"588","article-title":"Diagnostic of pathology on the vertebral column with embedded reject option","volume":"vol. 6669","author":"da Rocha Neto","year":"2011"},{"key":"10.1016\/j.patcog.2015.09.025_bib65","series-title":"Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond","author":"Sch\u00f6lkopf","year":"2002"},{"key":"10.1016\/j.patcog.2015.09.025_bib66","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1162\/089976698300017467","article-title":"Nonlinear component analysis as a kernel eigenvalue problem","volume":"10","author":"Sch\u00f6lkopf","year":"1998","journal-title":"Neural Comput."},{"key":"10.1016\/j.patcog.2015.09.025_bib67","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1007\/BF02344684","article-title":"Classification of breast tissue by electrical impedance spectroscopy","author":"Silva","year":"2000","journal-title":"Med. Biol. Eng. Comput."},{"key":"10.1016\/j.patcog.2015.09.025_bib68","first-page":"25","article-title":"Max-margin Markov networks","volume":"vol. 16","author":"Taskar","year":"2004"},{"key":"10.1016\/j.patcog.2015.09.025_bib69","doi-asserted-by":"crossref","first-page":"4658","DOI":"10.1016\/j.csda.2008.03.002","article-title":"Developing a feature weight self-adjustment mechanism for a k-means clustering algorithm","volume":"52","author":"Tsai","year":"2008","journal-title":"Comput. Stat. Data Anal."},{"key":"10.1016\/j.patcog.2015.09.025_bib70","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TNN.2005.845141","article-title":"Survey of clustering algorithms","volume":"16","author":"Xu","year":"2005","journal-title":"IEEE Trans. Neural Netw."},{"key":"10.1016\/j.patcog.2015.09.025_bib71","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1109\/21.299710","article-title":"Approximate clustering via the mountain method","volume":"24","author":"Yager","year":"1994","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"10.1016\/j.patcog.2015.09.025_bib72","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1109\/TSMCB.2003.810951","article-title":"Analysis of the weighting exponent in the fcm","volume":"34","author":"Yu","year":"2004","journal-title":"IEEE Trans. Syst. Man Cybern. Part B: Cybern."},{"key":"10.1016\/j.patcog.2015.09.025_bib73","unstructured":"D.Q. Zhang, S.C. Chen, Fuzzy clustering using kernel method, in: The 2002 International Conference on Control and Automation, 2002 ICCA, pp. 162\u2013163."},{"key":"10.1016\/j.patcog.2015.09.025_bib74","unstructured":"D.Q. Zhang, S.C. Chen, Kernel based fuzzy and possibilistic c-means clustering, in: Proceedings of the International Conference in Artificial Neural Network, pp. 122\u2013125."},{"key":"10.1016\/j.patcog.2015.09.025_bib75","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.artmed.2004.01.012","article-title":"A novel kernelized fuzzy c-means algorithm with application in medical image segmentation","volume":"32","author":"Zhang","year":"2004","journal-title":"Artif. Intell. Med."}],"container-title":["Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320315003568?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0031320315003568?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2019,8,31]],"date-time":"2019-08-31T05:27:42Z","timestamp":1567229262000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0031320315003568"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,3]]},"references-count":75,"alternative-id":["S0031320315003568"],"URL":"http:\/\/dx.doi.org\/10.1016\/j.patcog.2015.09.025","relation":{},"ISSN":["0031-3203"],"issn-type":[{"value":"0031-3203","type":"print"}],"subject":[],"published":{"date-parts":[[2016,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Kernel-based hard clustering methods with kernelization of the metric and automatic weighting of the variables","name":"articletitle","label":"Article Title"},{"value":"Pattern Recognition","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.patcog.2015.09.025","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"Copyright \u00a9 2015 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}]}}
