Fast approximate hierarchical clustering using similarity heuristics.

ABSTRACT:

Background

Agglomerative hierarchical clustering (AHC) is a common unsupervised data analysis technique used in several biological applications. Standard AHC methods require that all pairwise distances between data objects must be known. With ever-increasing data sizes this quadratic complexity poses problems that cannot be overcome by simply waiting for faster computers.

Results

We propose an approximate AHC algorithm HappieClust which can output a biologically meaningful clustering of a large dataset more than an order of magnitude faster than full AHC algorithms. The key to the algorithm is to limit the number of calculated pairwise distances to a carefully chosen subset of all possible distances. We choose distances using a similarity heuristic based on a small set of pi

SUBMITTER: Kull M 

PROVIDER: S-EPMC2561018 | biostudies-literature | 2008 Sep

REPOSITORIES: biostudies-literature