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)
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.
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.