Unsupervised clustering of series using dynamic programming

TL;DR

This paper introduces an unsupervised dynamic programming algorithm for segmenting and clustering multivariate series into coherent blocks based on a known physical model, demonstrated on petrophysical data.

Contribution

The paper presents a novel dynamic programming method for clustering series with constraints, ensuring coherence with a specified physical model.

Findings

Effective segmentation and clustering of multivariate series.

Application to petrophysical data using Waxman-Smits equation.

Algorithm respects constraints on cluster number, transitions, and block size.

Abstract

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.