  We consider the testing and estimation of change-points -- locations where
the distribution abruptly changes -- in a data sequence. A new approach, based
on scan statistics utilizing graphs representing the similarity between
observations, is proposed. The graph-based approach is non-parametric, and can
be applied to any data set as long as an informative similarity measure on the
sample space can be defined. Accurate analytic approximations to the
significance of graph-based scan statistics for both the single change-point
and the changed interval alternatives are provided. Simulations reveal that the
new approach has better power than existing approaches when the dimension of
the data is moderate to high. The new approach is illustrated on two
applications: The determination of authorship of a classic novel, and the
detection of change in a network over time.
