W. Aigner, S. Miksch, H. Schumann, and C. Tominski, Visualization of time-oriented data, 2011.
DOI : 10.1007/978-0-85729-079-3

P. K. Atrey, M. A. Hossain, A. Saddik, and M. S. Kankanhalli, Multimodal fusion for multimedia analysis: a survey, Multimedia Systems, vol.24, issue.11, pp.345-379, 2010.
DOI : 10.1115/1.3662552

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.190.1065

A. Blum and T. Mitchell, Combining labeled and unlabeled data with co-training, Proceedings of the eleventh annual conference on Computational learning theory , COLT' 98, pp.92-100, 1998.
DOI : 10.1145/279943.279962

URL : http://axon.cs.byu.edu/~martinez/classes/678/Papers/Mitchell_cotraining.pdf

L. N. Carroll, A. P. Au, L. T. Detwiler, T. C. Fu, I. S. Painter et al., Visualization and analytics tools for infectious disease epidemiology: A systematic review, Journal of Biomedical Informatics, vol.51, pp.287-298, 2014.
DOI : 10.1016/j.jbi.2014.04.006

C. Coello, G. Lamont, and D. Van-veldhuizen, Evolutionary algorithms for solving multi-objective problems, 2007.
DOI : 10.1007/978-1-4757-5184-0

J. A. Coy, J. H. Mehrkens, D. B. Roppenecker, and T. C. Lueth, Finding the center of Parkinson's disease. A novel measurement device for quantifying motor symptoms during DBS-surgery, 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), pp.1691-1696, 2014.
DOI : 10.1109/ROBIO.2014.7090578

M. Ehrgott, Multicriteria optimization, 2005.
DOI : 10.1007/978-3-662-22199-0

C. G. Goetz, W. Poewe, O. Rascol, C. Sampaio, G. T. Stebbins et al., Disorder Society Task Force on rating scales for Parkinson's disease, Movement Disorders, vol.324, issue.9, pp.1020-1028, 2004.
DOI : 10.1212/WNL.55.6.888

M. Gönen and E. Alpayd?n, Multiple kernel learning algorithms, Journal of Machine Learning Research, vol.12, pp.2211-2268, 2011.

A. Holzinger and I. Jurisica, Knowledge Discovery and Data Mining in Biomedical Informatics: The Future Is in Integrative, Interactive Machine Learning Solutions, Interactive Knowledge Discovery and Data Mining in Biomedical Informatics, pp.1-18, 2014.
DOI : 10.1073/pnas.1212247109

I. Kalamaras, A. Drosou, and D. Tzovaras, A multi-objective clustering approach for the detection of abnormal behaviors in mobile networks, 2015 IEEE International Conference on Communication Workshop (ICCW), pp.1491-1496, 2015.
DOI : 10.1109/ICCW.2015.7247390

I. Kalamaras, S. Papadopoulos, A. Drosou, and D. Tzovaras, MoVA: A Visual Analytics Tool Providing Insight in the Big Mobile Network Data, In: Artificial Intelligence Applications and Innovations, vol.45, issue.1, pp.383-396, 2015.
DOI : 10.1109/ICDE.2009.104

URL : https://hal.archives-ouvertes.fr/hal-01385373

Y. Y. Lin, T. L. Liu, and C. S. Fuh, Multiple kernel learning for dimensionality reduction . Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.33, issue.6, pp.1147-1160, 2011.

K. Nigam and R. Ghani, Analyzing the effectiveness and applicability of co-training, Proceedings of the ninth international conference on Information and knowledge management , CIKM '00
DOI : 10.1145/354756.354805

P. Ordóñez, M. Desjardins, M. Lombardi, C. U. Lehmann, and J. Fackler, An animated multivariate visualization for physiological and clinical data in the ICU, Proceedings of the 1st ACM International Health Informatics Symposium, pp.771-779, 2010.

C. Ramaker, J. Marinus, A. M. Stiggelbout, and B. J. Van-hilten, Systematic evaluation of rating scales for impairment and disability in Parkinson's disease, Movement Disorders, vol.47, issue.5, pp.867-876, 2002.
DOI : 10.1002/mds.10248

A. Rind, W. Aigner, S. Miksch, S. Wiltner, M. Pohl et al., Visually Exploring Multivariate Trends in Patient Cohorts Using Animated Scatter Plots, Ergonomics and Health Aspects of Work with Computers, pp.139-148, 2011.
DOI : 10.1007/978-3-642-21716-6_15

B. Snow, Objective measures for the progression of Parkinson's disease, Journal of Neurology, Neurosurgery & Psychiatry, vol.74, issue.3, pp.287-288, 2003.
DOI : 10.1136/jnnp.74.3.287

D. Stanev, P. Moschonas, K. Votis, D. Tzovaras, and K. Moustakas, Simulation and Visual Analysis of Neuromusculoskeletal Models and Data, In: Artificial Intelligence Applications and Innovations, vol.33, issue.6, pp.411-420, 2015.
DOI : 10.1007/s10439-005-3320-7

URL : https://hal.archives-ouvertes.fr/hal-01385375

H. Tong, J. He, M. Li, C. Zhang, and W. Y. Ma, Graph based multi-modality learning, Proceedings of the 13th annual ACM international conference on Multimedia , MULTIMEDIA '05, pp.862-871, 2005.
DOI : 10.1145/1101149.1101337

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.89.3299

C. Turkay, F. Jeanquartier, A. Holzinger, and H. Hauser, On computationallyenhanced visual analysis of heterogeneous data and its application in biomedical informatics, Interactive Knowledge Discovery and Data Mining in Biomedical Informatics, pp.117-140, 2014.

V. Landesberger, T. Kuijper, A. Schreck, T. Kohlhammer, J. Van-wijk et al., Visual Analysis of Large Graphs: State-of-the-Art and Future Research Challenges, Computer Graphics Forum, vol.6, issue.5, pp.1719-1749, 2011.
DOI : 10.1109/TVCG.2007.70515

URL : https://hal.archives-ouvertes.fr/hal-00712779

M. O. Ward, G. Grinstein, and D. Keim, Interactive data visualization: foundations, techniques, and applications, 2010.

K. Wongsuphasawat, J. A. Guerra-gómez, C. Plaisant, T. D. Wang, M. Taieb-maimon et al., LifeFlow, Proceedings of the 2011 annual conference on Human factors in computing systems, CHI '11, pp.1747-1756, 2011.
DOI : 10.1145/1978942.1979196