Abstract
BackgroundA 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.ResultsThe 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.ConclusionsIn this paper we present a new optimized implementation of the PFClust algorithm that runs considerably faster than the original.
Highlights
Cluster analysis [1] comprises methods designed to find structure in a dataset
One of the main challenges introduced by the lack of class labels is determining an optimal number of clusters that reflect the inherent structure present in the data
We have developed a novel clustering technique called PFClust [9] that automatically discovers an optimum partitioning of the data without requiring prior knowledge of the number of clusters
Summary
Cluster analysis [1] comprises methods designed to find structure in a dataset. Data can be divided into clusters that help us understand the problem domain, inform ongoing investigation, or form input for other data analysis techniques. * Correspondence: lazaros.mavridis.lm@gmail.com 2EaStCHEM School of Chemistry and Biomedical Sciences Research Complex, University of St Andrews, North Haugh, St Andrews, Scotland KY16 9ST, UK Full list of author information is available at the end of the article Method PFClust consists of two steps: threshold estimation and clustering.
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