Abstract
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 F test, the