PFClust: a novel parameter free clustering algorithm

Abstract

BackgroundWe present the algorithm PFClust (Parameter Free Clustering), which is able automatically to cluster data and identify a suitable number of clusters to group them into without requiring any parameters to be specified by the user. The algorithm partitions a dataset into a number of clusters that share some common attributes, such as their minimum expectation value and variance of intra-cluster similarity. A set of n objects can be clustered into any number of clusters from one to n, and there are many different hierarchical and partitional, agglomerative and divisive, clustering methodologies available that can be used to do this. Nonetheless, automatically determining the number of clusters present in a dataset constitutes a significant challenge for clustering algorithms. Identifying a putative optimum number of clusters to group the objects into involves computing and evaluating a range of clusterings with different numbers of clusters. However, there is no agreed or unique definition of optimum in this context. Thus, we test PFClust on datasets for which an external gold standard of ‘correct’ cluster definitions exists, noting that this division into clusters may be suboptimal according to other reasonable criteria. PFClust is heuristic in the sense that it cannot be described in terms of optimising any single simply-expressed metric over the space of possible clusterings.ResultsWe validate PFClust firstly with reference to a number of synthetic datasets consisting of 2D vectors, showing that its clustering performance is at least equal to that of six other leading methodologies – even though five of the other methods are told in advance how many clusters to use. We also demonstrate the ability of PFClust to classify the three dimensional structures of protein domains, using a set of folds taken from the structural bioinformatics database CATH.ConclusionsWe show that PFClust is able to cluster the test datasets a little better, on average, than any of the other algorithms, and furthermore is able to do this without the need to specify any external parameters. Results on the synthetic datasets demonstrate that PFClust generates meaningful clusters, while our algorithm also shows excellent agreement with the correct assignments for a dataset extracted from the CATH part-manually curated classification of protein domain structures.

Predicting the protein targets for athletic performance-enhancing substances

“…Figure  2 shows an example family from ChEMBL, one of the Androgen Receptor families (ChEMBL1871), with a number of different clusters of compounds. Splitting such a family into smaller groups based on ligand structure will allow us to identify the different sets of ligands; therefore PFClust [24] (brief description in Additional file 1) was applied to all the filtered ChEMBL families. We selected the PFClust algorithm because it is a parameter free clustering algorithm and does not require any kind of parameter tuning.…”

Predicting targets of compounds against neurological diseases using cheminformatic methodology

“…This leads to a set of refined families, each consisting of a group of molecules, which share similar chemical structure and bioactivity. The refined families of the ChEMBL dataset will allow us to identify the different sets of ligands [56,58,61].…”

“…Using these position vectors for each compound, we calculated the Euclidean distances between the resulting points and a similarity matrix was created. Finally, we clustered the vectors using PFClust [61].…”

“…This process generates bioactivity based filtered families. Our recently developed PFClust clustering [61] was applied to all the filtered ChEMBL families, which subdivided each family into smaller groups based both on ligand structure and their proven activity on a given protein target [56,58]. The compounds were clustered on the basis of their chemical structures, described by circular fingerprints (CFPs) [57].…”

A New Clustering Algorithm and Its Application in Assessing the Quality of Underground Water

“…Moreover, the abovementioned evaluation indices are distance-based measures; therefore, they can only evaluate the qualities of spherical clusters and cannot be used for arbitrary-shaped clusters. In [15], Mavridis et al proposed the algorithm PFClust (Parameter Free Clustering). e term "parameter free" means that the algorithm can automatically determine the number of clusters without requiring any user-defined parameters.…”