mskmeans: Maximally-Separated K-Means

In ArrayBin: Binarization of numeric data arrays

Description

Performs k-means clustering with initialization of centroids to partition data points around the data points with greatest magnitude difference

Usage

mskmeans(data,k=2)

Arguments

data	Numeric data input vector used to generate binary output
k	Number of clusters

Details

Function called by binarize.array. Calculates k-means (default k=2 gives binarization) classification around maximally-separated data points

Value

Discretized representation of data. For k=2, that is a numeric vector of the same length as input data, containing values 0 (representing a 'low' value) and 1 (respresenting a 'high' value).

Author(s)

Ed Curry [e-mail]

ArrayBin documentation built on May 1, 2019, 10:20 p.m.