evalCluster: evaulate the result of a clustering algorihm by comparing it...

In flowPeaks: An R package for flow data clustering

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.

Usage

evalCluster(gs,cand,method=c("Rand.index","Fmeasure","Vmeasure"),
                      rm.gs.outliers=TRUE)

Arguments

gs	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

Author(s)

Yongchao Ge [e-mail]

References

Ge Y. et al, flowPeaks: a fast unsupervised clustering for flow cytometry data via K-means and density peak finding, 2012, Bioinformatics 8(15):2052-8

See Also

flowPeaks

flowPeaks documentation built on Nov. 8, 2020, 5:02 p.m.