EM: Expectation-Maximization clustering method

In fdm2id: Data Mining and R Programming for Beginners

Expectation-Maximization clustering method

Description

Run the EM algorithm for clustering.

Usage

EM(d, k, model = "VVV", seed = NULL, ...)

Arguments

d	The dataset (matrix or data.frame).
k	Either an integer (the number of clusters) or a (vector) indicating the cluster to which each point is initially allocated.
model	A character string indicating the model. The help file for mclustModelNames describes the available models.
seed	A specified seed for random number generation (used only for the default k-means initialization).
...	Other parameters.

Value

A clustering model obtained by EM.

See Also

em, mstep, mclustModelNames

Examples

require (datasets)
data (iris)
EM (iris [, -5], 3) # Default initialization
km = KMEANS (iris [, -5], k = 3)
EM (iris [, -5], km$cluster) # Initialization with another clustering method

fdm2id documentation built on Sept. 4, 2026, 5:07 p.m.