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Table 1
Summary of various clustering approaches used in our empirical study
Clustering algorithms	Similarity measures	Approach to repeated data

Hierarchical agglomerative (average linkage, centroid linkage, complete linkage, single linkage)	Correlation/distance	Average over repeated measurements variability-weighted similarity. Force into the same subtree (FITSS)*
k-means	Correlation/distance	Average over repeated measurements variability-weighted similarity
CAST	Correlation/distance	Average over repeated measurements variability-weighted similarity
DIANA (hierarchical divisive)	Correlation/distance	Average over repeated measurements variability-weighted similarity
MCLUST-HC^†	None	Average over repeated measurements. Force into the same subtree (FITSS)*
IMM	None	Built-in error models (spherical, elliptical)

*FITSS refers to clustering the repeated measurements as individual objects and force the repeated measurements into the same subtrees. ^†MCLUST-HC denotes a model-based hierarchical clustering algorithm as implemented in the hcVVV function in the 2002 version of MCLUST.
Yeung et al. Genome Biology 2003 4:R34 doi:10.1186/gb-2003-4-5-r34

Table 1