RcmdrMisc (version 2.7-0)

KMeans: 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.

See Also

kmeans

Examples

# NOT RUN {
  data(USArrests)
  KMeans(USArrests, centers=3, iter.max=5, num.seeds=5)
# }