Clustering and causality inference using algorithmic complexity. Revolle, M., Cayre, F., & Le Bihan, N. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 843-847, Aug, 2017.
Clustering and causality inference using algorithmic complexity [pdf]Paper  doi  abstract   bibtex   
We present a set of algorithmic complexity estimates. We derive a normalized semi-distance that is shown to outperform the state-of-the-art. We also propose estimators for causality inference on directed acyclic graphs. Illustrative applications include clustering of human writing systems and causality assessment on novel drafts.

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