  Identification of local structure in intensive data -- such as time series,
images, and higher dimensional processes -- is an important problem in
astronomy. Since the data are typically generated by an inhomogeneous Poisson
process, an appropriate model is one that partitions the data space into cells,
each of which is described by a homogeneous (constant event rate) Poisson
process. It is key that the sizes and locations of the cells are determined by
the data, and are not predefined or even constrained to be evenly spaced. For
one-dimensional time series, the method amounts to Bayesian changepoint
detection. Three approaches to solving the multiple changepoint problem are
sketched, based on: (1) divide and conquer with single changepoints, (2)
maximum posterior for the number of changepoints, and (3) cell coalescence. The
last method starts from the Voronoi tessellation of the data, and thus should
easily generalize to spaces of higher dimension.
