KmeansAutoElbow: KmeansAutoElbow function

In uHMM: Construct an Unsupervised Hidden Markov Model

Description

KmeansAutoElbow performs k-means clustering on a dataframe with selection of optimal number of clusters using elbow criteria.

Usage

KmeansAutoElbow(features, Kmax, StopCriteria = 0.99, graph = FALSE)

Arguments

features	dataframe or matrix of raw data.
Kmax	maximum number of clusters allowed.
StopCriteria	elbow method cumulative explained variance > criteria to stop K-search. (???)
graph	boolean, if TRUE figures are plotted.

Details

KmeansAutoElbow returns partition and K number of groups according to kmeans clustering and Elbow method

Value

The function returns a list containing the following components:

K	number of clusters in data according to explained variance and kmeans algorithm.
res.kmeans	an object of class "kmeans" (see kmeans) containing classification results.

See Also

kmeans

Examples

x <- rbind(matrix(rnorm(300, mean = 0, sd = 0.3), ncol = 2), 
           matrix(rnorm(100, mean = 2, sd = 0.3), ncol = 2), 
           matrix(rnorm(100, mean = 4, sd = 0.3), ncol = 2))
colnames(x) <- c("x", "y")
km<-KmeansAutoElbow(x,round(dim(x)/25,0)[1],StopCriteria=0.99,graph=TRUE)
plot(x,col=km$res.kmeans$cluster)
points(km$res.kmeans$centers, col = 1:km$K, pch = 16)

uHMM documentation built on May 2, 2019, 9:16 a.m.