K-Means Clustering Using Multiple Random Seeds
Finds a number of k-means clusting solutions using R's
and selects as the final solution the one that has the minimum total
within-cluster sum of squared distances.
KMeans(x, centers, iter.max=10, num.seeds=10)
- 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).
- The number of clusters in the solution.
- The maximum number of iterations allowed.
- The number of different starting random seeds to use. Each random seed results in a different k-means solution.
- 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.
data(USArrests) KMeans(USArrests, centers=3, iter.max=5, num.seeds=5)
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