dp_means: Dirichlet Process K-Means Clustering

In abnormally-distributed/abdisttools: Random Functions for Personal Use

Description

Use the Bayesian Dirichlet process to perform K Means Clustering.

Usage

dp_means(data, disp.param = 2, iter = 4000, tolerance = 0.001)

Arguments

data	a data frame or matrix of numeric variables
disp.param	The dispersion parameter. Set to higher values to get fewer clusters. The dispersion parameter is the expected average width of the clusters. Defaults to 2, but you should try different values.
iter	number of iterations. Defaults to 4000.
tolerance	defaults to .0001

Examples

dp_means(iris[,1:4])

abnormally-distributed/abdisttools documentation built on May 5, 2019, 7:07 a.m.