PFClust: an optimised implementation of a parameter-free clustering algorithm.
BACKGROUND
A well-known problem in cluster analysis is finding an optimal number of clusters reflecting the inherent structure of the data. PFClust is a partitioning-based clustering algorithm capable, unlike many widely-used clustering algorithms, of automatically proposing an optimal number of clusters for the data.


RESULTS
The results of tests on various types of data showed that PFClust can discover clusters of arbitrary shapes, sizes and densities. The previous implementation of the algorithm had already been successfully used to cluster large macromolecular structures and small druglike compounds. We have greatly improved the algorithm by a more efficient implementation, which enables PFClust to process large data sets acceptably fast.


CONCLUSIONS
In this paper we present a new optimized implementation of the PFClust algorithm that runs considerably faster than the original.
Khadija
K
Musayeva
Tristan
T
Henderson
John Bo
JB
Mitchell
EaStCHEM School of Chemistry and Biomedical Sciences Research Complex, University of St Andrews, North Haugh, St Andrews, Scotland KY16 9ST, UK. lazaros.mavridis.lm@gmail.com.
Lazaros
L
Mavridis