KMwrapper: Wrapper to do K-means clustering

In btaschler/mcap: Model-Based Clustering in High Dimensions via Adaptive Projections

Description

Standard K-means clustering. The wrapper performs multiple restarts in case true labels are provided (in order to optimise cluster assignments w.r.t. the Rand index).

Usage

KMwrapper(xx, k, true_labels = NULL, verbose = FALSE)

Arguments

xx	The data matrix (n x p).
k	The number of clusters.
true_labels	Vector of true cluster assignments (when provided, it is used to compute the Rand index).
verbose	Logical, when true: print progress information.

Value

model_fit	Model fit (output of kmeans).
aRI	Adjusted Rand index (when true_labels is provided).

See Also

kmeans, adjustedRandIndex

Examples

  KMwrapper(xx = matrix(rnorm(500),50,10), k = 2)

btaschler/mcap documentation built on May 26, 2019, 1:31 a.m.