Clustering with shallow trees
Disordered Systems and Neural Networks
2015-05-14 v2 Data Structures and Algorithms
Quantitative Methods
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.
Keywords
Cite
@article{arxiv.0910.0767,
title = {Clustering with shallow trees},
author = {M. Bailly-Bechet and S. Bradde and A. Braunstein and A. Flaxman and L. Foini and R. Zecchina},
journal= {arXiv preprint arXiv:0910.0767},
year = {2015}
}
Comments
11 pages, 7 figures