{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T21:34:51Z","timestamp":1773092091435,"version":"3.50.1"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2015,8,7]],"date-time":"2015-08-07T00:00:00Z","timestamp":1438905600000},"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":["Soft Comput"],"published-print":{"date-parts":[[2016,9]]},"DOI":"10.1007\/s00500-015-1783-5","type":"journal-article","created":{"date-parts":[[2015,8,6]],"date-time":"2015-08-06T01:23:57Z","timestamp":1438824237000},"page":"3381-3392","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Multi-objective semi-supervised clustering of tissue samples for cancer diagnosis"],"prefix":"10.1007","volume":"20","author":[{"given":"Sriparna","family":"Saha","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kuldeep","family":"Kaushik","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abhay Kumar","family":"Alok","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sudipta","family":"Acharya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2015,8,7]]},"reference":[{"key":"1783_CR1","doi-asserted-by":"publisher","unstructured":"Acharya S, Saha S, Thadisina Y (2015) Multiobjective simulated annealing based clustering of tissue samples for cancer diagnosis. IEEE J Biomed Health Inform. doi: 10.1109\/JBHI.2015.2404971","DOI":"10.1109\/JBHI.2015.2404971"},{"issue":"6769","key":"1783_CR2","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1038\/35000501","volume":"403","author":"AA Alizadeh","year":"2000","unstructured":"Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudson JJ, Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Levy R, Wilson W, Grever MR, Byrd JC, Botstein D, Brown PO, Staudt LM (2000) Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403(6769):503\u2013511","journal-title":"Nature"},{"key":"1783_CR3","first-page":"33","volume-title":"Advances in neural information processing systems 18","author":"Y Altun","year":"2006","unstructured":"Altun Y, McAllester D, Belkin M (2006) Maximum margin semi-supervised learning for structured variables. In: Weiss Y, Sch\u00f6lkopf B, Platt J (eds) Advances in neural information processing systems 18. MIT Press, Cambridge, pp 33\u201340"},{"key":"1783_CR4","doi-asserted-by":"crossref","unstructured":"An L, Doerge RW (2012) Dynamic clustering of gene expression. ISRN Bioinform 2012(Article ID 537217):12 pages","DOI":"10.5402\/2012\/537217"},{"issue":"11","key":"1783_CR5","doi-asserted-by":"crossref","first-page":"1441","DOI":"10.1109\/TKDE.2008.79","volume":"20","author":"S Bandyopadhyay","year":"2008","unstructured":"Bandyopadhyay S, Saha S (2008) A point symmetry-based clustering technique for automatic evolution of clusters. IEEE Trans Knowl Data Eng 20(11):1441\u20131457","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"21","key":"1783_CR6","doi-asserted-by":"crossref","first-page":"2859","DOI":"10.1093\/bioinformatics\/btm418","volume":"23","author":"S Bandyopadhyay","year":"2007","unstructured":"Bandyopadhyay S, Mukhopadhyay A, Maulik U (2007) An improved algorithm for clustering gene expression data. Bioinformatics 23(21):2859\u20132865","journal-title":"Bioinformatics"},{"issue":"3","key":"1783_CR7","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1109\/TEVC.2007.900837","volume":"12","author":"S Bandyopadhyay","year":"2008","unstructured":"Bandyopadhyay S, Saha S, Maulik U, Deb K (2008) A simulated annealing-based multiobjective optimization algorithm: AMOSA. IEEE Trans Evol Comput 12(3):269\u2013283","journal-title":"IEEE Trans Evol Comput"},{"key":"1783_CR8","doi-asserted-by":"crossref","unstructured":"Basu S, Banjeree A, Mooney E, Banerjee A, Mooney RJ (2004) Active semi-supervision for pairwise constrained clustering. In: Proceedings of the 2004 SIAM international conference on data mining (SDM-04), pp 333\u2013344","DOI":"10.1137\/1.9781611972740.31"},{"key":"1783_CR9","doi-asserted-by":"crossref","unstructured":"Ben-Hur A, Guyon I (2003) Detecting stable clusters using principal component analysis. Methods Mol Biol 224:159\u2013182. http:\/\/view.ncbi.nlm.nih.gov\/pubmed\/12710673","DOI":"10.1385\/1-59259-364-X:159"},{"key":"1783_CR10","doi-asserted-by":"crossref","unstructured":"Bilenko M, Basu S, Mooney RJ (2004) Integrating constraints and metric learning in semi-supervised clustering. In: Proceedings of the twenty-first international conference on machine learning, ACM, pp 81\u201388","DOI":"10.1145\/1015330.1015360"},{"key":"1783_CR11","unstructured":"Chapelle O, Zien A (2005) Semi-supervised classification by low density separation. In: Cowell R, Ghahramani Z (eds) Proceedings of the tenth international workshop on artificial intelligence and statistics, pp 57\u201364. http:\/\/eprints.pascal-network.org\/archive\/00000388\/"},{"key":"1783_CR12","doi-asserted-by":"crossref","DOI":"10.7551\/mitpress\/9780262033589.001.0001","volume-title":"Semi-supervised learning. Adaptive computation and machine learning","author":"O Chapelle","year":"2006","unstructured":"Chapelle O, Sch\u00f6lkopf B, Zien A (2006) Semi-supervised learning. Adaptive computation and machine learning. MIT Press, Cambridge"},{"key":"1783_CR13","doi-asserted-by":"crossref","unstructured":"de Souto MCP, Costa IG, de Araujo DSA, Ludermir TB, Schliep A (2008) Clustering cancer gene expression data: a comparative study. BMC Bioinform 9. http:\/\/dblp.uni-trier.de\/db\/journals\/bmcbi\/bmcbi9.html#SoutoCALS08","DOI":"10.1186\/1471-2105-9-497"},{"issue":"2","key":"1783_CR14","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182\u2013197. doi: 10.1109\/4235.996017","journal-title":"IEEE Trans Evol Comput"},{"issue":"5439","key":"1783_CR15","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1126\/science.286.5439.531","volume":"286","author":"TR Golub","year":"1999","unstructured":"Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286(5439):531\u2013537","journal-title":"Science"},{"key":"1783_CR16","volume-title":"Algorithms for clustering data","author":"AK Jain","year":"1988","unstructured":"Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall, Upper Saddle River"},{"issue":"3","key":"1783_CR17","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1145\/331499.331504","volume":"31","author":"AK Jain","year":"1999","unstructured":"Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264\u2013323","journal-title":"ACM Comput Surv"},{"issue":"12","key":"1783_CR18","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1109\/TPAMI.2002.1114856","volume":"24","author":"U Maulik","year":"2002","unstructured":"Maulik U, Bandyopadhyay S (2002) Performance evaluation of some clustering algorithms and validity indices. IEEE Trans Pattern Anal Mach Intell 24(12):1650\u20131654","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1783_CR19","doi-asserted-by":"publisher","unstructured":"Mukhopadhyay A, Bandyopadhyay S, Maulik U (2010) Multi-class clustering of cancer subtypes through SVM based ensemble of Pareto-optimal solutions for gene marker identification. PLoS One 5(11):e13803. doi: 10.1371\/journal.pone.0013803","DOI":"10.1371\/journal.pone.0013803"},{"issue":"1","key":"1783_CR20","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.asoc.2012.08.005","volume":"13","author":"S Saha","year":"2013","unstructured":"Saha S, Bandyopadhyay S (2013) A generalized automatic clustering algorithm in a multiobjective framework. Appl Soft Comput 13(1):89\u2013108","journal-title":"Appl Soft Comput"},{"key":"1783_CR21","doi-asserted-by":"crossref","unstructured":"Saha S, Ekbal A, Alok AK (2012) Semi-supervised clustering using multiobjective optimization. In: 2th International Conference on hybrid intelligent systems (HIS), 2012, IEEE, pp 360\u2013365","DOI":"10.1109\/HIS.2012.6421361"},{"key":"1783_CR22","doi-asserted-by":"publisher","unstructured":"Strehl A, Ghosh J (2003) Cluster ensembles\u2014a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3:583\u2013617. doi: 10.1162\/153244303321897735","DOI":"10.1162\/153244303321897735"},{"key":"1783_CR23","doi-asserted-by":"crossref","unstructured":"Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander ES, Golub TR (1999) Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci USA 96:2907\u20132912","DOI":"10.1073\/pnas.96.6.2907"},{"key":"1783_CR24","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-2440-0","volume-title":"The nature of statistical learning theory","author":"VN Vapnik","year":"1995","unstructured":"Vapnik VN (1995) The nature of statistical learning theory. Springer, New York"},{"issue":"1","key":"1783_CR25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1756-0381-7-7","volume":"7","author":"Y Wang","year":"2014","unstructured":"Wang Y, Pan Y (2014) Semi-supervised consensus clustering for gene expression data analysis. BioData Min 7(1):1\u201313","journal-title":"BioData Min"},{"issue":"12","key":"1783_CR26","doi-asserted-by":"crossref","first-page":"R83","DOI":"10.1186\/gb-2003-4-12-r83","volume":"4","author":"K Yeung","year":"2003","unstructured":"Yeung K, Bumgarner R (2003) Multiclass classification of microarray data with repeated measurements: application to cancer. Genome Biol 4(12):R83","journal-title":"Genome Biol"},{"key":"1783_CR27","unstructured":"Yeung K, Ruzzo W (2001) An empirical study on principal component analysis for clustering gene expression data. http:\/\/citeseerx.ist.psu.edu\/viewdoc\/summary?doi=10.1.1.28.8391"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-015-1783-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00500-015-1783-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-015-1783-5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,28]],"date-time":"2019-08-28T23:17:04Z","timestamp":1567034224000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00500-015-1783-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2015,8,7]]},"references-count":27,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2016,9]]}},"alternative-id":["1783"],"URL":"https:\/\/doi.org\/10.1007\/s00500-015-1783-5","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2015,8,7]]}}}