A cost function for similarity-based hierarchical clustering
Data Structures and Algorithms
2015-10-20 v1 Machine Learning
Machine Learning
Abstract
The development of algorithms for hierarchical clustering has been hampered by a shortage of precise objective functions. To help address this situation, we introduce a simple cost function on hierarchies over a set of points, given pairwise similarities between those points. We show that this criterion behaves sensibly in canonical instances and that it admits a top-down construction procedure with a provably good approximation ratio.
Cite
@article{arxiv.1510.05043,
title = {A cost function for similarity-based hierarchical clustering},
author = {Sanjoy Dasgupta},
journal= {arXiv preprint arXiv:1510.05043},
year = {2015}
}