  This manuscript makes two contributions to the field of change-point
detection. In a generalchange-point setting, we provide a generic algorithm for
aggregating local homogeneity testsinto an estimator of change-points in a time
series. Interestingly, we establish that the errorrates of the collection of
tests directly translate into detection properties of the
change-pointestimator. This generic scheme is then applied to various problems
including covariance change-point detection, nonparametric change-point
detection and sparse multivariate mean change-point detection. For the latter,
we derive minimax optimal rates that are adaptive to theunknown sparsity and to
the distance between change-points when the noise is Gaussian. Forsub-Gaussian
noise, we introduce a variant that is optimal in almost all sparsity regimes.
