Unsupervised clustering of series using dynamic programming

Karthigan Sinnathamby, Chang-Yu Hou, Lalitha Venkataramanan, Vasileios-Marios Gkortsas, François Fleuret

Published 2021-01-23Version 1

We are interested in clustering parts of a given single multi-variate series in an unsupervised manner. We would like to segment and cluster the series such that the resulting blocks present in each cluster are coherent with respect to a known model (e.g. physics model). Data points are said to be coherent if they can be described using this model with the same parameters. We have designed an algorithm based on dynamic programming with constraints on the number of clusters, the number of transitions as well as the minimal size of a block such that the clusters are coherent with this process. We present an use-case: clustering of petrophysical series using the Waxman-Smits equation.