Unsupervised clustering of series using dynamic programming

Unsupervised clustering is a branch of machine learning that aims to categorize the data based on the self-similarity. In other word, data-points in the same group (called a cluster) are more similar to each other than to those in other groups. This task can be achieved by various algorithms (the well-known K-means or spectral clustering but also hierarchical clustering [1] or density-based clustering [2]) that differ significantly in their understanding of what constitutes a cluster and how to efficiently find them. In many cases, there exist models/functions, governed by a finite set of parameters, providing either physics or phenomenology correlations between input data. The presence of these models can in principle be used to characterize clusters (cluster characterization) because one can define a loss function to measure how well a point belongs to this cluster (cluster affiliation).