A. Banerjee, S. Merugu, I. S. Dhillon, and J. Ghosh, Clustering with bregman divergences, Journal of Machine Learning Research, vol.6, pp.1705-1749, 2005.

S. Basu, A. Banerjee, and R. J. Mooney, Semi-supervised clustering by seeding, ICML. pp, pp.27-34, 2002.

S. Basu, M. Bilenko, and R. J. Mooney, A probabilistic framework for semi-supervised clustering, pp.59-68, 2004.

M. Bilenko, S. Basu, and R. J. Mooney, Integrating constraints and metric learning in semi-supervised clustering, ICML, pp.81-88, 2004.

M. Cucuringu, I. Koutis, S. Chawla, G. L. Miller, and R. Peng, Simple and scalable constrained clustering: a generalized spectral method, AISTATS. pp, pp.445-454, 2016.

I. Davidson and S. S. Ravi, Intractability and clustering with constraints, ICML. pp, pp.201-208, 2007.

J. V. Davis, B. Kulis, P. Jain, S. Sra, and I. S. Dhillon, Information-theoretic metric learning, ICML, pp.209-216, 2007.

W. Haiyan, Y. Haomin, L. Xueming, and R. Haijun, Semi-Supervised Autoencoder: A Joint Approach of Representation and Classification, CICN, pp.1424-1430, 2015.

B. Harwood, G. , V. K. Carneiro, G. Reid, I. D. Drummond et al., Smart mining for deep metric learning, ICCV, pp.2840-2848, 2017.

L. Hubert and P. Arabie, Comparing partitions, Journal of Classification, vol.2, issue.1, pp.193-218, 1985.

D. Ienco and R. G. Pensa, Semi-supervised clustering with multiresolution autoencoders, IJCNN. pp, pp.1-8, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01931471

W. Kalintha, S. Ono, M. Numao, and K. Fukui, Kernelized evolutionary distance metric learning for semi-supervised clustering, pp.4945-4946, 2017.

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, 2014.

D. Klein, S. D. Kamvar, and C. D. Manning, From instance-level constraints to spacelevel constraints: Making the most of prior knowledge in data clustering, pp.307-314, 2002.

N. Kumar and K. Kummamuru, Semisupervised clustering with metric learning using relative comparisons, IEEE Trans. Knowl. Data Eng, vol.20, issue.4, pp.496-503, 2008.

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.521, p.436, 2015.

L. Van-der-maaten and G. E. Hinton, Visualizing high-dimensional data using t-sne, vol.9, pp.2579-2605, 2008.

E. Min, X. Guo, Q. Liu, G. Zhang, J. Cui et al., A survey of clustering with deep learning: From the perspective of network architecture, IEEE Access, vol.6, pp.39501-39514, 2018.

B. M. Nogueira, Y. K. Tomas, and R. M. Marcacini, Integrating distance metric learning and cluster-level constraints in semi-supervised clustering, IJCNN. pp, pp.4118-4125, 2017.

A. Rasmus, M. Berglund, M. Honkala, H. Valpola, and T. Raiko, Semi-supervised learning with ladder networks, NIPS, pp.3546-3554, 2015.

Y. Ren, K. Hu, X. Dai, L. Pan, S. C. Hoi et al., Semi-supervised deep embedded clustering, Neurocomputing, vol.325, pp.121-130, 2019.

A. Strehl and J. Ghosh, Cluster ensembles -A knowledge reuse framework for combining multiple partitions, Journal of Machine Learning Research, vol.3, pp.583-617, 2002.

K. Wagstaff, C. Cardie, S. Rogers, and S. Schrödl, Constrained k-means clustering with background knowledge, ICML, pp.577-584, 2001.

H. Xiao, K. Rasul, and R. Vollgraf, Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017.

J. Xie, R. B. Girshick, and A. Farhadi, Unsupervised deep embedding for clustering analysis, ICML. pp, pp.478-487, 2016.

B. Yu, T. Liu, M. Gong, C. Ding, and D. Tao, Correcting the triplet selection bias for triplet loss, ECCV, pp.71-86, 2018.

Y. Zhao, Z. Jin, G. Qi, H. Lu, and X. Hua, An adversarial approach to hard triplet generation, ECCV, pp.508-524, 2018.

X. Zhu, C. C. Loy, and S. Gong, Constrained clustering with imperfect oracles, IEEE Trans. Neural Netw. Learning Syst, vol.27, issue.6, pp.1345-1357, 2016.