Contraction Clustering (RASTER): paper and reference implementations

One of the results of my work at the Fraunhofer-Chalmers Research Centre for Industrial Mathematics (FCC) was the discovery of a very fast special-purpose linear-time clustering algorithm, Contraction Clustering (RASTER). I presented our work at the Third International Conference on Machine Learning, Optimization and Big Data (MOD 2017) in Volterra, Italy, earlier this month.

At FCC we decided to create reference implementations in a number of programming languages. You will find reference implementations of RASTER in Python, Erlang, Haskell, and Scala in my GitLab repository. Note that this is a mirror of the official FCC source code release.

Our paper will appear in the Proceedings of MOD 2017, which are part of the Springer Lecture Notes in Computer Science, early next year. The submitted self-archived manuscript of our RASTER paper is likewise available on my GitLab account.

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