binarizeKMeans: k-means Binarization

In BiTrinA: Binarization and Trinarization of One-Dimensional Data

k-means Binarization

Description

Binarizes a vector of real-valued data using the k-means clustering algorithm. The data is first split into 2 clusters.The values belonging to the cluster with the smaller centroid are set to 0, and the values belonging to the greater centroid are set to 1.

Usage

binarize.kMeans(vect, 
                nstart=1, 
                iter.max=10,
                dip.test=TRUE,
                na.rm=FALSE)

Arguments

vect	A real-valued vector to be binarized (at least 3 measurements).
nstart	The number of restarts for k-means. See kmeans for details.
iter.max	The maximum number of iterations for k-means. See kmeans for details.
dip.test	If set to TRUE, Hartigan's dip test for unimodality is performed on vect, and its p-value is returned in the pvalue slot of the result. An insignificant test indicates that the data may not be binarizeable.
na.rm	If set to TRUE, NA values are removed from the input. Otherwise, binarization will fail in the presence of NA values.

Value

Returns an object of class BinarizationResult.

See Also

kmeans, BinarizationResult, BoolNet::binarizeTimeSeries()

Examples

result <- binarize.kMeans(iris[,"Petal.Length"])

print(result)
plot(result, twoDimensional=TRUE)

BiTrinA documentation built on Oct. 2, 2023, 5:07 p.m.