Unsupervised clustering of series using dynamic programming
This work addresses the challenge of clustering series data for domains like geophysics, but it appears incremental as it applies a known algorithmic approach (dynamic programming) to a specific use-case.
The paper tackles the problem of unsupervised clustering of multivariate series by segmenting and grouping parts based on coherence with a known model, such as a physics model, using a dynamic programming algorithm with constraints on clusters, transitions, and block sizes, and demonstrates its application in petrophysical series clustering with the Waxman-Smits equation.
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.