We present a new quartet heuristic for

hierarchical clustering

from a given distance matrix.

We determine a dendrogram (ternary tree)

by a new quartet

method and a fast heuristic to implement it.

We do not assume that there is a true ternary tree that generated the

distances and which we with to recover as closeley as possible.

Our aim is to model the distance matrix as faithfully as possible

by the dendrogram. Our algorithm is essentially

randomized hill-climbing, using

parallellized Genetic Programming, where

undirected trees evolve in a random walk

driven by a prescribed fitness function.

Our method is capable of handling up to 60--80

objects in a matter of hours, while no existing quartet heuristic

can directly compute a quartet tree of more than about 20--30 objects

without running for years.

The method is implemented and available as public software

at www.complearn.org. We present applications in many areas

like music, literature, bird-flu (H5N1) virus clustering, and automatic

meaning discovery using Google.