Maximum Likelihood Estimation for Single Linkage Hierarchical Clustering

Autor:	Zhu, Dekang, Guralnik, Dan P., Wang, Xuezhi, Li, Xiang, Moran, Bill
Rok vydání:	2015
Předmět:	Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We derive a statistical model for estimation of a dendrogram from single linkage hierarchical clustering (SLHC) that takes account of uncertainty through noise or corruption in the measurements of separation of data. Our focus is on just the estimation of the hierarchy of partitions afforded by the dendrogram, rather than the heights in the latter. The concept of estimating this "dendrogram structure'' is introduced, and an approximate maximum likelihood estimator (MLE) for the dendrogram structure is described. These ideas are illustrated by a simple Monte Carlo simulation that, at least for small data sets, suggests the method outperforms SLHC in the presence of noise. Comment: 15 pages, 6 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1511.07944 Zobrazit plný text záznamu View this record from Arxiv