| KMeans {RcmdrMisc} | 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
See Also
Examples
data(USArrests)
KMeans(USArrests, centers=3, iter.max=5, num.seeds=5)