C. Bouveyron, S. Girard, and C. Schmid, High-dimensional data clustering, Computational Statistics and Data Analysis, vol.52, issue.1, pp.502-519, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00548573

A. Cheam, M. Marbac, and P. Mcnicholas, Model-based clustering for spatiotemporal data on air quality monitoring, Environmetrics, vol.28, issue.3, 2017.

S. Dabo-niang, A. Yao, L. Pischedda, P. Cuny, and F. Gilbert, Spatial mode estimation for functional random fields with application to bioturbation problem, Stochastic Environmental Research and Risk Assessment, vol.24, issue.4, pp.487-497, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00431794

A. Delaigle and P. Hall, Defining probability density for a distribution of random functions, The Annals of Statistics, pp.1171-1193, 2010.

R. Giraldo, P. Delicado, and J. Mateu, Ordinary kriging for function-valued spatial data, Environ. Ecol. Stat, vol.18, issue.3, pp.411-426, 2011.

R. Giraldo, P. Delicado, and J. Mateu, Hierarchical clustering of spatially correlated functional data, Statistica Neerlandica, vol.66, issue.4, pp.403-421, 2012.

J. Jacques and C. Preda, Advances in Data Analysis and Classification, vol.8, p.20, 2014.

. Vincent, Joint work with Cristian PREDA 2,3 and Sophie DABO 2,4 Clustering spatial functional data Introduction Model based clustering clustering of spatial functional data Application on Ozone concentration data Conclusion and perspectives References

E. Romano, J. Mateu, and R. Giraldo, On the performance of two clustering methods for spatial functional data, AStA Adv. Stat. Anal, vol.99, issue.4, pp.467-492, 2015.

E. Romano, A. Balzanella, and R. Verde, Spatial variability clustering for spatially dependent functional data, Stat. Comput, vol.27, issue.3, pp.645-658, 2017.

M. D. Ruiz-medina, R. M. Espejo, and E. Romano, Spatial functional normal mixed effect approach for curve classification, Advances in Data Analysis and Classification, vol.8, issue.3, pp.257-285, 2014.