  Self organizing maps (SOMs) are widely-used for unsupervised classification.
For this application, they must be combined with some partitioning scheme that
can identify boundaries between distinct regions in the maps they produce. We
discuss a novel partitioning scheme for SOMs based on the Bayesian Blocks
segmentation algorithm of Scargle [1998]. This algorithm minimizes a cost
function to identify contiguous regions over which the values of the attributes
can be represented as approximately constant. Because this cost function is
well-defined and largely independent of assumptions regarding the number and
structure of clusters in the original sample space, this partitioning scheme
offers significant advantages over many conventional methods. Sample code is
available.
