<p>This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</p> <p>Kernel density smoothing techniques have been used in classification or supervised learning of gene expression profile (GEP) data, but their applications to clustering
or unsupervised learning of those data have not been explored and assessed. Here
we report a kernel density clustering method for analysing GEP data and compare
its performance with the three most widely-used clustering methods: hierarchical
clustering, K-means clustering, and multivariate mixture model-based clustering.
Using several methods to measure agreement, between-cluster isolation, and withincluster
coherence, such as the Adjusted Rand Index, the Pseudo <italic>F</italic> test, the r<sup>2</sup> test,
and the profile plot, we have assessed the effectiveness of kernel density clustering
for recovering clusters, and its robustness against noise on clustering both simulated
and real GEP data. Our results show that the kernel density clustering method has
excellent performance in recovering clusters from simulated data and in grouping
large real expression profile data sets into compact and well-isolated clusters, and
that it is the most robust clustering method for analysing noisy expression profile
data compared to the other three methods assessed.</p>
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Comp Funct Genomics. 2003 Jun; 4(3):287-299