  We present a new approach for detecting related crime series, by unsupervised
learning of the latent feature embeddings from narratives of crime record via
the Gaussian-Bernoulli Restricted Boltzmann Machines (RBM). This is a
drastically different approach from prior work on crime analysis, which
typically considers only time and location and at most category information.
After the embedding, related cases are closer to each other in the Euclidean
feature space, and the unrelated cases are far apart, which is a good property
can enable subsequent analysis such as detection and clustering of related
cases. Experiments over several series of related crime incidents hand labeled
by the Atlanta Police Department reveal the promise of our embedding methods.
