R: K-Means Clustering Using Multiple Random Seeds

KMeans {Rcmdr}

R Documentation

K-Means Clustering Using Multiple Random Seeds

Description

Finds a number of k-means clusting solutions using R's kmeans function, and selects as the final solution the one that has the minimum total within-cluster sum of squared distances.

Usage

KMeans(x, centers, iter.max=10, num.seeds=10)

Arguments

`x`	A numeric matrix of data, or an object that can be coerced to such a matrix (such as a numeric vector or a dataframe with all numeric columns).
`centers`	The number of clusters in the solution.
`iter.max`	The maximum number of iterations allowed.
`num.seeds`	The number of different starting random seeds to use. Each random seed results in a different k-means solution.

Value

A list with components:

`cluster`	A vector of integers indicating the cluster to which each point is allocated.
`centers`	A matrix of cluster centres (centroids).
`withinss`	The within-cluster sum of squares for each cluster.
`tot.withinss`	The within-cluster sum of squares summed across clusters.
`betweenss`	The between-cluster sum of squared distances.
`size`	The number of points in each cluster.

Author(s)

Dan Putler

Examples

  data(USArrests)
  KMeans(USArrests, centers=3, iter.max=5, num.seeds=5)

[Package Rcmdr version 1.1-6 Index]