F. Bach and Z. Harchoui, DIFFRAC: a discriminative and flexible framework for clustering, Adv. NIPS, 2008.

S. X. Chen, Q. Kim, J. G. Lin, E. P. Carbonell, and . Xing, Graph-structured multi-task regression and an efficient optimization method for general fused lasso, 2010.

C. [. Cormen, R. L. Leiserson, C. Rivest, and . Stein, Introduction to Algorithms, 2001.

T. [. Efron, I. Hastie, R. Johnstone, and . Tibshirani, Least angle regression, Annals of statistics, vol.32, issue.2, pp.40-99, 2004.

J. Friedman, T. Hastie, H. Hoefling, and R. Tibshirani, Pathwise coordinate optimization, The Annals of Applied Statistics, vol.1, issue.2, pp.30-32, 2007.
DOI : 10.1214/07-AOAS131
URL : http://arxiv.org/abs/0708.1485

T. [. Fujishige, S. Hayashi, and . Isotani, The minimum-norm-point algorithm applied to submodular function minimization and linear programming, 2006.

M. Frank and P. Wolfe, An algorithm for quadratic programming, Naval Research Logistics Quarterly, vol.3, issue.1-2, p.95110, 1956.
DOI : 10.1002/nav.3800030109

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

H. Hoefling, A Path Algorithm for the Fused Lasso Signal Approximator, Journal of Computational and Graphical Statistics, vol.19, issue.4, 2009.
DOI : 10.1198/jcgs.2010.09208

C. [. Krause and . Guestrin, Beyond convexity: Submodularity in machine learning, IJCAI, 2009.

F. Lindsten, H. Ohlsson, and L. Ljung, Clustering using sum-of-norms regularization: With application to particle filter output computation, 2011 IEEE Statistical Signal Processing Workshop (SSP), 2011.
DOI : 10.1109/SSP.2011.5967659

J. Mattingley and S. Boyd, CVXMOD: Convex optimization software in Python (web page and software), 2008.

M. [. Ng, Y. Jordan, and . Weiss, On spectral clustering: Analysis and an algorithm, Adv. NIPS, 2001.

J. [. Rosset and . Zhu, Piecewise linear regularized solution paths, The Annals of Statistics, vol.35, issue.3, pp.1012-1030, 2007.
DOI : 10.1214/009053606000001370

H. [. Shen and . Huang, Grouping Pursuit Through a Regularization Solution Surface, Journal of the American Statistical Association, vol.105, issue.490, pp.727-739, 2010.
DOI : 10.1198/jasa.2010.tm09380

]. R. Tib96 and . Tibshirani, Regression Shrinkage and Selection Via the Lasso, J. R. Statist. Soc. B, vol.58, issue.1, pp.267-288, 1996.

M. [. Tibshirani and . Saunders, Sparsity and smoothness via the fused lasso, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.99, issue.1, pp.9-17, 2005.
DOI : 10.1016/S0140-6736(02)07746-2

G. [. Tibshirani, T. Walther, and . Hastie, Estimating the number of clusters in a data set via the gap statistic, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.63, issue.2, pp.41-64, 2001.
DOI : 10.1111/1467-9868.00293

[. Vert and K. Bleakley, Fast detection of multiple change-points shared by many signals using group LARS, Adv. NIPS, 2010.

J. [. Xu, B. Neufeld, D. Larson, and . Schuurmans, Maximum margin clustering Model selection and estimation in regression with grouped variables, Journal of the Royal Statistical Society, issue.B, pp.684-691, 2004.

G. [. Zhao, B. Rocha, and . Yu, The composite absolute penalties family for grouped and hierarchical variable selection, The Annals of Statistics, vol.37, issue.6A, pp.3468-3497, 2009.
DOI : 10.1214/07-AOS584