evalCluster: evaulate the result of a clustering algorihm
by comparing it with the gold
standard
Description
This function takes the cluster labels of the two clusterings, one is
based on the gold standard, the other is a candidate clusterign, and
compute one of the three metrics to assess the candidate clustering performance.
A integer-valued vector of length n for the cluster labels of
the gold standard clustering, where negative numbers
such as -1 is for the outerliers
cand
A integer-valued vector of length n for the cluster label of
a candidate clustering, where -1 is for the outliers
rm.gs.outliers
Determining whether the outliers of the gold
standard clustering should be removed in the comparison
method
A single character to indicate which one of three metrics
should be used to evaluate the clustering. The details are described in
Ge (2012) and references mentioned in that paper
Rand.index
The adjusted Rand.index
Fmeasure
F-measure
Vmeasure
V-measure
References
Ge Y. et al, flowPeaks: a fast unsupervised clustering for flow
cytometry data via K-means and density peak finding, 2012, Bioinformatics, in press.