12758

Convex Clustering: An Attractive Alternative to Hierarchical Clustering

Gary K. Chen, Eric Chi, John Ranola, Kenneth Lange
Department of Preventive Medicine (Biostatistics Division), University of Southern California, CA
arXiv:1409.2065 [q-bio.GN], (6 Sep 2014)

@article{2014arXiv1409.2065C,

   author={Chen}, G.~K. and {Chi}, E. and {Ranola}, J. and {Lange}, K.},

   title={"{Convex Clustering: An Attractive Alternative to Hierarchical Clustering}"},

   journal={ArXiv e-prints},

   archivePrefix={"arXiv"},

   eprint={1409.2065},

   primaryClass={"q-bio.GN"},

   keywords={Quantitative Biology – Genomics},

   year={2014},

   month={sep},

   adsurl={http://adsabs.harvard.edu/abs/2014arXiv1409.2065C},

   adsnote={Provided by the SAO/NASA Astrophysics Data System}

}

The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical clustering visually appealing and in tune with their evolutionary perspective. Hierarchical clustering operates on multiple scales simultaneously. This is essential, for instance, in transcriptome data where one may be interested in making qualitative inferences about how lower-order relationships like gene modules lead to higher-order relationships like pathways or biological processes. The recently developed method of convex clustering preserves the visual appeal of hierarchical clustering while ameliorating its propensity to make false inferences in the presence of outliers and noise. The current paper exploits the proximal distance principle to construct a novel algorithm for solving the convex clustering problem. The solution paths generated by convex clustering reveal relationships between clusters that are hidden by static methods such as k-means clustering. Our convex clustering software separates parameters, accommodates missing data, and supports prior information on relationships. The software is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed. Several biological examples illustrate the strengths of convex clustering and the ability of the proximal distance algorithm to handle high-dimensional problems.
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