  We propose a change-point detection method for large scale multiple testing
problems with data having clustered signals. Unlike the classic change-point
setup, the signals can vary in size within a cluster. The clustering structure
on the signals enables us to effectively delineate the boundaries between
signal and non-signal segments. New test statistics are proposed for
observations from one and/or multiple realizations. Their asymptotic
distributions are derived. We also study the associated variance estimation
problem. We allow the variances to be heteroscedastic in the multiple
realization case, which substantially expands the applicability of the proposed
method. Simulation studies demonstrate that the proposed approach has a
favorable performance. Our procedure is applied to {an array based Comparative
Genomic Hybridization (aCGH)} dataset.
