BibTeX
@MISC{Cilibrasi09clustering,
author = {Rudi Cilibrasi and Paul M. B. Vitányi},
title = {Clustering},
year = {2009}
}
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Abstract
The problem is to construct an optimal weight tree from the 3 () n 4 weighted quartet topologies on n objects, where optimality means that the summed weight of the embedded quartet topologies is optimal (so it can be the case that the optimal tree embeds all quartets as nonoptimal topologies). We present a Monte Carlo heuristic, based on randomized hill climbing, for approximating the optimal weight tree, given the quartet topology weights. The method repeatedly transforms a bifurcating tree, with all objects involved as leaves, achieving a monotonic approximation to the exact single globally optimal tree. The method has been extensively used for general hierarchical clustering of nontreelike (non-phylogeny) data in various domains and across domains with heterogenous data, and is implemented and available, as part of the CompLearn package. We compare performance and running time with those of UPGMA, BioNJ, and NJ, as implemented in the SplitsTree package on genomic data for which the latter are optimized.
