testAlgsMinSize: Choose Optimal Clustering Algorithms
In COMMUNAL: Robust Selection of Cluster Number K

Description Usage Arguments Details Value Author(s) See Also Examples

These functions serve to find locally optimal clustering algorithms for an output of clusterRange. They measure the percentage of clusterings for which a given algorithm returned any clusters with minSize or fewer members.

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testAlgsMinSize(clusRange, algs = "all", minSize = 3)

getGoodAlgs(clusRange, algs = "all", minSize = 3)

`clusRange`	The output from a call to `clusterRange`.
`algs`	The algorithms to examine. Defaults to "all" algorithms present in clusRange, but user can define a subset (character vector).
`minSize`	The size at or below which a cluster is considered sub-optimal.

testAlgsMinSize iterates over the range of sub-datasets present in clusRange, and will print cluster assignment counts for all K where a cluster smaller than minSize has been returned (to help the user identify patterns in the data). This makes it quite verbose. It will then return the mean percentage of clusters < minSize for all algorithms.

getGoodAlgs is a wrapper for testAlgsMinSize that returns the algorithms that are at or under the mean for percent clusterings < minSize.

testAlgsMinSize returns a named numeric vector of mean percentage of clusters < minSize.

getGoodAlgs returns a character vector of algorithms that are at or under the mean for percent clusterings < minSize, suitable for passing into plotRange3D

Sweeney

getNonCorrNonMonoMeasures

## output from running \code{clusterRange} on data(BRCA.100)
data(BRCA.results)

## BRCA results does not have any clusters < minSize=3, so returns all.
testAlgsMinSize(BRCA.results)
getGoodAlgs(BRCA.results)

## force output, call minSize=50 (just to test here)
getGoodAlgs(BRCA.results, minSize=50)