Clustering with label constrained Dirichlet process mixture model

Banfield, 1993, Model-based Gaussian and non-Gaussian clustering, Biometrics, 49, 803, 10.2307/2532201 Basu, 2008 Bilenko, 2004, Integrating constraints and metric learning in semi-supervised clustering, 11 Blackwell, 1973, Ferguson distributions via Pólya urn schemes, Ann. Statist., 1, 353, 10.1214/aos/1176342372 Collins, 1998, Design and construction of a realistic digital brain phantom, IEEE Trans. Med. Imaging, 17, 463, 10.1109/42.712135 Davidson, 2005, Clustering with constraints: Feasibility issues and the k-means algorithm, 138 Dawid, 1979, Maximum likelihood estimation of observer error-rates using the em algorithm, J. R. Stat. Soc. Ser. C (Appl. Stat.), 28, 20 Dua, 2019 Escobar, 1995, BayesIan density estimation and inference using mixtures, J. Amer. Statist. Assoc., 90, 577, 10.1080/01621459.1995.10476550 Ferguson, 1973, A Bayesian analysis of some nonparametric problems, Ann. Statist., 1, 209, 10.1214/aos/1176342360 Fraley, 2002, Model-based clustering, discriminant analysis, and density estimation, J. Amer. Statist. Assoc., 97, 611, 10.1198/016214502760047131 Hart, 2014, Pattern classification of response inhibition in ADHD: toward the development of neurobiological markers for ADHD, Human Brain Mapp., 35, 3083, 10.1002/hbm.22386 Hartigan, 1990, Partition models, Comm. Statist. Theory Methods, 19, 2745, 10.1080/03610929008830345 Jain, 1999, Data clustering: A review, ACM Comput. Surv., 31, 264, 10.1145/331499.331504 Kwan, 1999, MRI simulation-based evaluation of image-processing and classification methods, IEEE Trans. Med. Imaging, 18, 1085, 10.1109/42.816072 Lelis, 2009, Semi-supervised density-based clustering, 842 Liu, 2018, Partition level constrained clustering, IEEE Trans. Pattern Anal. Mach. Intell., 40, 2469, 10.1109/TPAMI.2017.2763945 Lo, 1984, On a class of Bayesian nonparametric estimates: I. Density estimates, Ann. Statist., 12, 351, 10.1214/aos/1176346412 Lu, 2005, Semi-supervised learning with penalized probabilistic clustering, 849 Müller, 2011, A product partition model with regression on covariates, J. Comput. Graph. Statist., 20, 260, 10.1198/jcgs.2011.09066 Neal, 1998, A view of the EM algorithm that justifies incremental, sparse, and other variants, 355 Pedrycz, 1985, Algorithms of fuzzy clustering with partial supervision, Pattern Recognit. Lett., 3, 13, 10.1016/0167-8655(85)90037-6 Pedrycz, 1997, Fuzzy clustering with partial supervision, IEEE Trans. Syst. Man Cybern. B, 27, 787, 10.1109/3477.623232 Pelleg, 2007, K-means with large and noisy constraint sets, 674 Quintana, 2003, BayesIan clustering and product partition models, J. R. Stat. Soc. Ser. B Stat. Methodol., 65, 557, 10.1111/1467-9868.00402 Shental, 2003, Computing Gaussian mixture models with EM using equivalence constraints, 465 Strehl, 2002, Cluster ensembles—A knowledge reuse framework for combining multiple partitions, J. Mach. Learn. Res., 3, 583 Wagstaff, 2000, Clustering with instance-level constraints, 1103 Wagstaff, 2001, Constrained K-means clustering with background knowledge, 577 Xu, 2005, Survey of clustering algorithms, IEEE Trans. Neural Netw., 16, 645, 10.1109/TNN.2005.845141 Yi, 2012, Crowdclustering with sparse pairwise labels: A matrix completion approach Zhou, 2012, Learning from the wisdom of crowds by minimax entropy, 2195 Zijdenbos, 1994, Morphometric analysis of white matter lesions in MR images: method and validation, IEEE Trans. Med. Imaging, 13, 716, 10.1109/42.363096