Clustering with shallow trees

TL;DR

This paper introduces a hierarchical clustering method using shallow trees optimized via message passing, bridging single linkage and Affinity Propagation, with applications to biological and medical datasets.

Contribution

The paper presents a novel clustering approach that interpolates between existing methods, offering efficient optimization and new insights into complex biological data.

Findings

Effective clustering of biological datasets

Provides new insights through interpolation technique

Efficient message-passing algorithm for shallow trees

Abstract

We propose a new method for hierarchical clustering based on the optimisation of a cost function over trees of limited depth, and we derive a message--passing method that allows to solve it efficiently. The method and algorithm can be interpreted as a natural interpolation between two well-known approaches, namely single linkage and the recently presented Affinity Propagation. We analyze with this general scheme three biological/medical structured datasets (human population based on genetic information, proteins based on sequences and verbal autopsies) and show that the interpolation technique provides new insight.