R. Experiments, 12 4.1 Chapter Outline, Parameters Setting, vol.12, issue.42, p.12

A. Metrics and .. , 31 A.1 31 A.2 Mean Closest Point (MCP), p.32

B. Algorithms and .. , 34 B.1 K-means, p.35

C. Scores and .. , 41 C.1 Inertia, 41 C.2 Rand Index, p.42

G. Auzias, DISCO: A Coherent Diffeomorphic Framework for Brain Registration under Exhaustive Sulcal Constraints, LNCS, vol.5761, pp.730-738, 2009.
DOI : 10.1007/978-3-642-04268-3_90

URL : https://hal.archives-ouvertes.fr/hal-00534432

W. Matthan, L. J. Caan, . Van-vliet, B. Charles, . Majoie et al., Nonrigid point set matching of white matter tracts for diffusion tensor image analysis, IEEE Trans Biomed Eng, vol.58, issue.9, pp.2431-2440, 2011.

E. Lynn and . Delisi, Early detection of schizophrenia by diffusion weighted imaging, Psychiatry Research: Neuroimaging, vol.148, issue.1, pp.61-66, 2006.

S. Durrleman, P. Fillard, X. Pennec, A. Trouvé, and N. Ayache, A Statistical Model of White Matter Fiber Bundles Based on Currents, Inf Process Med Imaging, vol.26, issue.11, pp.114-125, 2009.
DOI : 10.1007/11566465_25

URL : https://hal.archives-ouvertes.fr/inria-00502721

M. Ester, H. Peter-kriegel, S. Jörg, and X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, pp.226-231, 1996.

E. Garyfallidis, QuickBundles, a Method for Tractography Simplification, Frontiers in Neuroscience, vol.6, issue.175, p.2012
DOI : 10.3389/fnins.2012.00175

P. Guevara, D. Duclap, C. Poupon, L. Marrakchi-kacem, P. Fillard et al., Automatic fiber bundle segmentation in massive tractography datasets using a multi-subject bundle atlas, NeuroImage, vol.61, issue.4, pp.611083-1099, 2012.
DOI : 10.1016/j.neuroimage.2012.02.071

URL : https://hal.archives-ouvertes.fr/hal-00700800

P. Guevara, C. Poupon, D. Rivière, Y. Cointepas, M. Descoteaux et al., Robust clustering of massive tractography datasets, NeuroImage, vol.54, issue.3, pp.1975-1993, 2011.
DOI : 10.1016/j.neuroimage.2010.10.028

L. Hubert and P. Arabie, Comparing partitions, Journal of Classification, vol.78, issue.1, pp.193-218, 1985.
DOI : 10.1007/BF01908075

H. Li, Z. Xue, L. Guo, T. Liu, J. Hunter et al., A hybrid approach to automatic clustering of white matter fibers, NeuroImage, vol.49, issue.2, pp.1249-1258, 2010.
DOI : 10.1016/j.neuroimage.2009.08.017

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proc. Fifth Berkeley Symp, pp.281-297, 1967.

M. Maddah, W. Eric, L. Grimson, K. Simon, . Warfield et al., A unified framework for clustering and quantitative analysis of white matter fiber tracts, Medical Image Analysis, vol.12, issue.2, pp.191-202, 2008.
DOI : 10.1016/j.media.2007.10.003

M. Maddah, V. James, . Miller, V. Edith, A. Sullivan et al., Sheet-Like White Matter Fiber Tracts: Representation, Clustering, and Quantitative Analysis, Med Image Comput Comput Assist Interv, vol.14, issue.5, pp.191-199, 2011.
DOI : 10.1016/j.media.2010.05.002

W. Glenn, M. C. Milligan, and . Cooper, A study of the comparability of external criteria for hierarchical cluster analysis, Multivariate Behavioral Research, vol.21, issue.4, pp.441-458, 1986.

B. Moberts, A. Vilanova, and J. J. Van-wijk, Evaluation of fiber clustering methods for diffusion tensor imaging, Visualization, 2005. VIS 05. IEEE, pp.65-72, 2005.

A. Myronenko, X. Song, and M. Carreira-perpinan, Non-rigid point set registration: Coherent point drift, Advances in Neural Information Processing Systems (NIPS) 19, pp.1009-1016, 2007.

L. J. O-'donnell, M. Kubicki, M. E. Shenton, M. H. Dreusicke, W. E. Grimson et al., A method for clustering white matter fiber tracts, AJNR Am J Neuroradiol, vol.27, issue.5, pp.1032-1036, 2006.

F. Pedregosa, Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

M. William and . Rand, Objective criteria for the evaluation of clustering methods, Journal of the American Statistical Association, vol.66, issue.336, pp.846-850, 1971.

A. Rosenberg and J. Hirschberg, V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure, Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp.410-420, 2007.

J. Peter and . Rousseeuw, Silhouettes: A graphical aid to the interpretation and validation of cluster analysis, JCAM, vol.20, issue.0, pp.53-65, 1987.

D. Sculley, Web-scale k-means clustering, Proceedings of the 19th international conference on World wide web, WWW '10, pp.1177-1178, 2010.
DOI : 10.1145/1772690.1772862

V. Siless, S. Medina, G. Varoquaux, and B. Thirion, A Comparison of Metrics and Algorithms for Fiber Clustering, 2013 International Workshop on Pattern Recognition in Neuroimaging, pp.190-193, 2013.
DOI : 10.1109/PRNI.2013.56

URL : https://hal.archives-ouvertes.fr/hal-00858115

V. Siless, Joint T1 and Brain Fiber Log-Demons Registration Using Currents to Model Geometry, MICCAI, pp.57-65, 2012.
DOI : 10.1007/978-3-642-33418-4_8

URL : https://hal.archives-ouvertes.fr/hal-00723367

V. Siless, P. Guevara, X. Pennec, and P. Fillard, Joint T1 and Brain Fiber Diffeomorphic Registration Using the Demons, Multimodal Brain Image Analysis, pp.10-18, 2011.
DOI : 10.1007/978-3-642-24446-9_2

URL : https://hal.archives-ouvertes.fr/hal-00828387

H. Steinhaus, Sur la division des corp materiels en parties, Bull. Acad. Polon. Sci, vol.1, pp.801-804, 1956.

N. Toussaint, J. C. Souplet, and P. Fillard, Medinria: Medical image navigation and research tool by inria, Proc. of MICCAI'07 Workshop on Interaction in medical image analysis and visualization, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00616047

J. Nguyen-xuan-vinh, J. Epps, and . Bailey, Information theoretic measures for clusterings comparison: is a correction for chance necessary?, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.1073-1080, 2009.

Q. Wang, G. Pew-thian-yap, D. Wu, and . Shen, Application of neuroanatomical features to tractography clustering, Human Brain Mapping, vol.14, issue.9, 2012.
DOI : 10.1002/hbm.22051

X. Wang, W. Eric, L. Grimson, and C. Westin, Tractography segmentation using a hierarchical Dirichlet processes mixture model, NeuroImage, vol.54, issue.1, pp.290-302, 2011.
DOI : 10.1016/j.neuroimage.2010.07.050

D. Wassermann, L. Bloy, E. Kanterakis, R. Verma, and R. Deriche, Unsupervised white matter fiber clustering and tract probability map generation: Applications of a Gaussian process framework for white matter fibers, NeuroImage, vol.51, issue.1, pp.228-241, 2010.
DOI : 10.1016/j.neuroimage.2010.01.004

URL : https://hal.archives-ouvertes.fr/inria-00496898

Y. Xia, U. Turken, L. Susan, . Whitfield-gabrieli, D. John et al., Knowledgebased classification of neuronal fibers in entire brain, Med Image Comput Comput Assist Interv, vol.8, pp.205-212, 2005.

O. Zvitia, A. Mayer, R. Shadmi, S. Miron, K. Hayit et al., Co-registration of White Matter Tractographies by Adaptive-Mean-Shift and Gaussian Mixture Modeling, IEEE Transactions on Medical Imaging, vol.29, issue.1, pp.132-145, 2010.
DOI : 10.1109/TMI.2009.2029097