kmeanspp | R Documentation |
Use the kmeans++ algorithm to cluster points
into k
clusters, as implemented in the
deprecated LICORS package, using the
built-in function kmeans.
kmeanspp(data, k = 2, iter.max = 100, nstart = 10, ...)
data |
An N \times d matrix, where there are N samples in dimension d. |
k |
The number of clusters. |
iter.max |
The maximum number of iterations. |
nstart |
The number of restarts. |
... |
Additional arguments passed to |
A list with 9 entries:
cluster
: A vector of integers from 1:k indicating the
cluster to which each point is allocated.
centers
: A matrix of cluster centers.
totss
: The total sum of squares.
withinss
: Vector of within-cluster sum of squares,
one component per cluster.
tot.withinss
: Total within-cluster sum of squares,
i.e.sum(withinss).
betweenss
: The between-cluster sum of squares,
i.e.totss-tot.withinss.
size
: The number of points in each cluster.
iter
: The number of (outer) iterations.
ifault
: An integer indicator of a possible algorithm problem.
initial.centers
: The initial centers used.
Arthur, D. and S. Vassilvitskii (2007). “k-means++: The advantages of careful seeding.” In H. Gabow (Ed.), Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms [SODA07], Philadelphia, pp. 1027-1035. Society for Industrial and Applied Mathematics.
kmeans
set.seed(1984) n <- 100 X = matrix(rnorm(n), ncol = 2) Y = matrix(runif(length(X)*2, -1, 1), ncol = ncol(X)) Z = rbind(X, Y) cluster_Z = kmeanspp(Z, k = 5)
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