{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T09:19:21Z","timestamp":1758273561802},"reference-count":44,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T00:00:00Z","timestamp":1557964800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T00:00:00Z","timestamp":1557964800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Adv Data Anal Classif"],"published-print":{"date-parts":[[2020,3]]},"DOI":"10.1007\/s11634-019-00359-6","type":"journal-article","created":{"date-parts":[[2019,5,16]],"date-time":"2019-05-16T13:27:31Z","timestamp":1558013251000},"page":"29-56","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Learning a metric when clustering data points in the presence of constraints"],"prefix":"10.1007","volume":"14","author":[{"given":"Ahmad Ali","family":"Abin","sequence":"first","affiliation":[]},{"given":"Mohammad Ali","family":"Bashiri","sequence":"additional","affiliation":[]},{"given":"Hamid","family":"Beigy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,5,16]]},"reference":[{"key":"359_CR1","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.patrec.2016.10.013","volume":"84","author":"AA Abin","year":"2016","unstructured":"Abin AA (2016a) Clustering with side information: further efforts to improve efficiency. Pattern Recognit Lett 84:252\u2013258","journal-title":"Pattern Recognit Lett"},{"key":"359_CR2","first-page":"1","volume":"99","author":"AA Abin","year":"2016","unstructured":"Abin AA (2016b) Querying beneficial constraints before clustering using facility location analysis. IEEE Trans Cybern 99:1\u201312","journal-title":"IEEE Trans Cybern"},{"issue":"3","key":"359_CR3","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1016\/j.patcog.2013.09.034","volume":"47","author":"AA Abin","year":"2014","unstructured":"Abin AA, Beigy H (2014) Active selection of clustering constraints: a sequential approach. Pattern Recognit 47(3):1443\u20131458","journal-title":"Pattern Recognit"},{"issue":"3","key":"359_CR4","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1016\/j.patcog.2014.09.008","volume":"48","author":"AA Abin","year":"2015","unstructured":"Abin AA, Beigy H (2015) Active constrained fuzzy clustering: a multiple kernels learning approach. Pattern Recognit 48(3):953\u2013967","journal-title":"Pattern Recognit"},{"issue":"3","key":"359_CR5","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1016\/j.physrep.2008.09.002","volume":"469","author":"A Arenas","year":"2008","unstructured":"Arenas A, Diaz-Guilera A, Kurths J, Moreno Y, Zhou C (2008) Synchronization in complex networks. Phys Rep 469(3):93\u2013153","journal-title":"Phys Rep"},{"key":"359_CR6","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1016\/j.patrec.2014.02.014","volume":"45","author":"MS Baghshah","year":"2014","unstructured":"Baghshah MS, Afsari F, Shouraki SB, Eslami E (2014) Scalable semi-supervised clustering by spectral kernel learning. Pattern Recognit Lett 45:161\u2013171","journal-title":"Pattern Recognit Lett"},{"key":"359_CR7","first-page":"937","volume":"6","author":"A Bar-Hillel","year":"2005","unstructured":"Bar-Hillel A, Hertz T, Shental N, Weinshall D (2005) Learning a Mahalanobis metric from equivalence constraints. J Mach Learn Res 6:937\u2013965","journal-title":"J Mach Learn Res"},{"key":"359_CR8","doi-asserted-by":"crossref","unstructured":"Basu S, Banerjee A, Mooney RJ (2004) Active semi-supervision for pairwise constrained clustering. In: Proceedings of the 5th SIAM international conference on data mining, ICDM \u201904, pp 333\u2013344","DOI":"10.1137\/1.9781611972740.31"},{"key":"359_CR9","doi-asserted-by":"crossref","DOI":"10.1201\/9781584889977","volume-title":"Constrained clustering: advances in algorithms, theory, and applications","author":"S Basu","year":"2008","unstructured":"Basu S, Davidson I, Wagstaff KL (2008) Constrained clustering: advances in algorithms, theory, and applications. Chapman and Hall\/CRC, Boca Raton"},{"key":"359_CR10","doi-asserted-by":"crossref","unstructured":"Bilenko M, Basu S, Mooney RJ (2004a) Integrating constraints and metric learning in semi-supervised clustering. In: Proceedings of the 21th international conference on Machine learning, ICML \u201904, pp 11\u201318","DOI":"10.1145\/1015330.1015360"},{"key":"359_CR11","doi-asserted-by":"crossref","unstructured":"Bilenko M, Basu S, Mooney RJ (2004b) Integrating constraints and metric learning in semi-supervised clustering. In: Proceedings of the twenty-first international conference on Machine learning, ACM, p\u00a011","DOI":"10.1145\/1015330.1015360"},{"key":"359_CR12","doi-asserted-by":"crossref","unstructured":"Chang H, yan Yeung D (2004) Locally linear metric adaptation for semi-supervised clustering. In: Proceedings of the 21th international conference on machine learning, ICML \u201904, pp 153\u2013160","DOI":"10.1145\/1015330.1015391"},{"issue":"1","key":"359_CR13","doi-asserted-by":"publisher","first-page":"90","DOI":"10.14778\/1453856.1453871","volume":"1","author":"H Cheng","year":"2008","unstructured":"Cheng H, Hua KA, Vu K (2008) Constrained locally weighted clustering. Proc VLDB Endow 1(1):90\u2013101","journal-title":"Proc VLDB Endow"},{"key":"359_CR14","first-page":"115","volume-title":"Lecture Notes in Computer Science","author":"Ian Davidson","year":"2006","unstructured":"Davidson I, Wagstaff KL, Basu S (2006) Measuring constraint-set utility for partitional clustering algorithms. In: Proceedings of the 10th European conference on Principle and practice of knowledge discovery in databases, PKDD \u201906, pp 115\u2013126"},{"issue":"1","key":"359_CR15","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s00521-012-1207-8","volume":"24","author":"S Ding","year":"2014","unstructured":"Ding S, Jia H, Zhang L, Jin F (2014) Research of semi-supervised spectral clustering algorithm based on pairwise constraints. Neural Comput Appl 24(1):211\u2013219","journal-title":"Neural Comput Appl"},{"key":"359_CR16","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.neucom.2014.02.053","volume":"139","author":"C Gong","year":"2014","unstructured":"Gong C, Fu K, Wu Q, Tu E, Yang J (2014) Semi-supervised classification with pairwise constraints. Neurocomputing 139:130\u2013137","journal-title":"Neurocomputing"},{"issue":"2","key":"359_CR17","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1016\/j.patcog.2013.07.023","volume":"47","author":"P He","year":"2014","unstructured":"He P, Xu X, Hu K, Chen L (2014) Semi-supervised clustering via multi-level random walk. Pattern Recognit 47(2):820\u2013832","journal-title":"Pattern Recognit"},{"key":"359_CR18","doi-asserted-by":"crossref","unstructured":"Hertz T, Bar-hillel A, Weinshall D (2004) Boosting margin based distance functions for clustering. In: Proceedings of the 21th international conference on machine learning, ICML \u201904, pp 393\u2013400","DOI":"10.1145\/1015330.1015389"},{"key":"359_CR19","doi-asserted-by":"crossref","unstructured":"Hoi SCH, Jin R, Lyu MR (2007) Learning non-parametric kernel matrices from pairwise constraints. In: Proceedings of the 24th international conference on Machine learning, ICML \u201907, pp 361\u2013368","DOI":"10.1145\/1273496.1273542"},{"issue":"1","key":"359_CR20","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/BF01908075","volume":"2","author":"L Hubert","year":"1985","unstructured":"Hubert L, Arabie P (1985) Comparing Partitions. J Classif 2(1):193\u2013218","journal-title":"J Classif"},{"key":"359_CR21","first-page":"519","volume":"13","author":"P Jain","year":"2012","unstructured":"Jain P, Kulis B, Davis JV, Dhillon IS (2012) Metric and kernel learning using a linear transformation. J Mach Learn Res 13:519\u2013547","journal-title":"J Mach Learn Res"},{"issue":"5","key":"359_CR22","doi-asserted-by":"publisher","first-page":"656","DOI":"10.1016\/j.patrec.2010.12.014","volume":"32","author":"M Kalakech","year":"2011","unstructured":"Kalakech M, Biela P, Macaire L, Hamad D (2011) Constraint scores for semi-supervised feature selection: a comparative study. Pattern Recognit Lett 32(5):656\u2013665","journal-title":"Pattern Recognit Lett"},{"key":"359_CR23","doi-asserted-by":"crossref","unstructured":"Khoreva A, Galasso F, Hein M, Schiele B (2014) Learning must-link constraints for video segmentation based on spectral clustering. In: Pattern recognition, Springer, pp 701\u2013712","DOI":"10.1007\/978-3-319-11752-2_58"},{"issue":"3","key":"359_CR24","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1007\/s10994-013-5397-9","volume":"94","author":"AN Korinna Bade","year":"2014","unstructured":"Korinna Bade AN (2014) Hierarchical constraints. Mach Learn 94(3):371\u2013399","journal-title":"Mach Learn"},{"issue":"1","key":"359_CR25","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10994-008-5084-4","volume":"74","author":"B Kulis","year":"2009","unstructured":"Kulis B, Basu S, Dhillon I, Mooney R (2009) Semi-supervised graph clustering: a kernel approach. Mach Learn 74(1):1\u201322","journal-title":"Mach Learn"},{"key":"359_CR26","unstructured":"LeCun Y, Cortes C (2010) MNIST handwritten digit database \nhttp:\/\/yann.lecun.com\/exdb\/mnist\/"},{"issue":"7","key":"359_CR27","doi-asserted-by":"publisher","first-page":"1299","DOI":"10.1109\/TPAMI.2011.217","volume":"34","author":"H Liu","year":"2012","unstructured":"Liu H, Wu Z, Li X, Cai D, Huang T (2012) Constrained non-negative matrix factorization for image representation. IEEE Trans Pattern Anal Mach Intell 34(7):1299\u20131311","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"359_CR28","doi-asserted-by":"publisher","first-page":"327","DOI":"10.1007\/s11634-015-0200-3","volume":"10","author":"V Melnykov","year":"2016","unstructured":"Melnykov V, Melnykov I, Michael S (2016) Semi-supervised model-based clustering with positive and negative constraints. Adv Data Anal Classif 10(3):327\u2013349","journal-title":"Adv Data Anal Classif"},{"issue":"4","key":"359_CR29","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1207\/s15327906mbr2104_5","volume":"21","author":"G Milligan","year":"1986","unstructured":"Milligan G, Cooper M (1986) A study of the comparability of external criteria for hierarchical cluster analysis. Multivariate Behav Res 21(4):441\u2013458","journal-title":"Multivariate Behav Res"},{"key":"359_CR30","doi-asserted-by":"crossref","unstructured":"Okabe M, Yamada S (2009) Clustering with constrained similarity learning. In: Proceedings of the 2009 IEEE\/WIC\/ACM international conference on web intelligence and international conference on intelligent agent technology, pp 30\u201333","DOI":"10.1109\/WI-IAT.2009.223"},{"key":"359_CR31","doi-asserted-by":"publisher","first-page":"2323","DOI":"10.1126\/science.290.5500.2323","volume":"290","author":"ST Roweis","year":"2000","unstructured":"Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323\u20132326","journal-title":"Science"},{"key":"359_CR32","doi-asserted-by":"publisher","first-page":"217","DOI":"10.1162\/089976602753284509","volume":"14","author":"J Sinkkonen","year":"2002","unstructured":"Sinkkonen J, Kaski S (2002) Clustering based on conditional distributions in an auxiliary space. Neural Comput 14:217\u2013239","journal-title":"Neural Comput"},{"issue":"3","key":"359_CR33","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1007\/s11634-016-0254-x","volume":"11","author":"M Smieja","year":"2017","unstructured":"Smieja M, Wiercioch M (2017) Constrained clustering with a complex cluster structure. Adv Data Anal Classif 11(3):493\u2013518","journal-title":"Adv Data Anal Classif"},{"key":"359_CR34","doi-asserted-by":"publisher","first-page":"2982","DOI":"10.1016\/j.patcog.2010.02.022","volume":"43","author":"M Soleymani Baghshah","year":"2010","unstructured":"Soleymani Baghshah M, Bagheri Shouraki S (2010) Non-linear metric learning using pairwise similarity and dissimilarity constraints and the geometrical structure of data. Pattern Recognit 43:2982\u20132992","journal-title":"Pattern Recognit"},{"issue":"1","key":"359_CR35","first-page":"57","volume":"98","author":"D Truong","year":"2013","unstructured":"Truong D, Battiti R (2013) A flexible cluster-oriented alternative clustering algorithm for choosing from the pareto front of solutions. Mach Learn 98(1):57\u201391","journal-title":"Mach Learn"},{"issue":"4","key":"359_CR36","doi-asserted-by":"publisher","first-page":"1749","DOI":"10.1016\/j.patcog.2011.10.016","volume":"45","author":"VV Vu","year":"2012","unstructured":"Vu VV, Labroche N, Bouchon-Meunier B (2012) Improving constrained clustering with active query selection. Pattern Recognit 45(4):1749\u20131758","journal-title":"Pattern Recognit"},{"key":"359_CR37","unstructured":"Wagstaff K, Cardie C (2000) Clustering with instance-level constraints. In: Proceedings of the seventeenth national conference on artificial intelligence and twelfth conference on on innovative applications of artificial intelligence, July 30\u2013August 3, 2000, Austin, Texas, USA, p 1097"},{"key":"359_CR38","unstructured":"Wagstaff K, Cardie C, Rogers S, Schr\u00f6dl S (2001) Constrained k-means clustering with background knowledge. In: Proceedings of the 18th international conference on machine learning, ICML \u201901, pp 577\u2013584"},{"issue":"9","key":"359_CR39","doi-asserted-by":"publisher","first-page":"2576","DOI":"10.1016\/j.patcog.2013.02.015","volume":"46","author":"Q Wang","year":"2013","unstructured":"Wang Q, Yuen PC, Feng G (2013) Semi-supervised metric learning via topology preserving multiple semi-supervised assumptions. Pattern Recognit 46(9):2576\u20132587","journal-title":"Pattern Recognit"},{"key":"359_CR40","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.neucom.2013.12.027","volume":"135","author":"S Wu","year":"2014","unstructured":"Wu S, Feng X, Zhou W (2014) Spectral clustering of high-dimensional data exploiting sparse representation vectors. Neurocomputing 135:229\u2013239","journal-title":"Neurocomputing"},{"issue":"12","key":"359_CR41","doi-asserted-by":"publisher","first-page":"3600","DOI":"10.1016\/j.patcog.2008.05.018","volume":"41","author":"S Xiang","year":"2008","unstructured":"Xiang S, Nie F, Zhang C (2008) Learning a Mahalanobis distance metric for data clustering and classification. Pattern Recognit 41(12):3600\u20133612","journal-title":"Pattern Recognit"},{"key":"359_CR42","doi-asserted-by":"crossref","unstructured":"Ye J, Zhao Z, Liu H (2007) Adaptive distance metric learning for clustering. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition (CVPR 2007), pp 1\u20137","DOI":"10.1109\/CVPR.2007.383103"},{"issue":"4","key":"359_CR43","doi-asserted-by":"publisher","first-page":"1320","DOI":"10.1016\/j.patcog.2009.11.005","volume":"43","author":"X Yin","year":"2010","unstructured":"Yin X, Chen S, Hu E, Zhang D (2010) Semi-supervised clustering with metric learning: an adaptive kernel method. Pattern Recognit 43(4):1320\u20131333","journal-title":"Pattern Recognit"},{"key":"359_CR44","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.neunet.2012.09.010","volume":"36","author":"Z Zhang","year":"2012","unstructured":"Zhang Z, Zhao M, Chow TWS (2012) Marginal semi-supervised sub-manifold projections with informative constraints for dimensionality reduction and recognition. Neural Netw 36:97\u2013111","journal-title":"Neural Netw"}],"container-title":["Advances in Data Analysis and Classification"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11634-019-00359-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s11634-019-00359-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s11634-019-00359-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,5,14]],"date-time":"2020-05-14T23:24:58Z","timestamp":1589498698000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s11634-019-00359-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,5,16]]},"references-count":44,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,3]]}},"alternative-id":["359"],"URL":"https:\/\/doi.org\/10.1007\/s11634-019-00359-6","relation":{},"ISSN":["1862-5347","1862-5355"],"issn-type":[{"value":"1862-5347","type":"print"},{"value":"1862-5355","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,5,16]]},"assertion":[{"value":"8 February 2018","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 February 2019","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 May 2019","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 May 2019","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}