kmeans_cluster: kmeans_cluster

In zhaodyleo/Kmeans: The package will perform the kmeans cluster on the data matrix

Description

Perform k-means clustering on a data matrix.

Usage

kmeans_cluster(X, center, max.iter = 2500, tol = 1e-08)

Arguments

X	numeric matrix of data, or an object that can be coerced to such a matrix (such as a numeric vector or a data frame with all numeric columns).
center	either the number of clusters, say \(k\), or a set of initial (distinct) cluster centres. If a number, a random center will be assigned.
max.iter	the maximum number of iterations allowed.
tol	Parameters for determining convergence.

Value

a list contains centres of each cluster and the labels for each observation

Examples

# One Dimension Example
x1 <-  runif(10000,0,1)
x2 <-  runif(10000,2,3)
x3 <-  runif(10000,4,5)
train_data <-  c(x1,x2,x3)
train_label <-  c(rep(0,10000),rep(1,10000),rep(2,10000))
training_result <-  kmeans_cluster(train_data,3,2500,1e-8)

# Multi Dimension Example
X1 <-  runif(10000,0,1)
X2 <-  runif(10000,0,1)
X3 <-  runif(10000,3,5)
X4 <-  runif(10000,3,5)
train_data <-  cbind(c(X1,X3),c(X2,X4))
train_label <-  c(rep(0,10000),rep(1,10000))
training_result<-  kmeans_cluster(train_data,2,2500,1e-8)

zhaodyleo/Kmeans documentation built on Dec. 23, 2021, 9:18 p.m.