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January 2, 2021 10:20
k-means++ (written in R; with the Euclidean distance; distance computation is vectorized)
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kmeansp2 <- function(x, k, iter.max = 10, nstart = 1, ...) { | |
n <- nrow(x) # number of data points | |
centers <- numeric(k) # IDs of centers | |
distances <- matrix(numeric(n * (k - 1)), ncol = k - 1) # distances[i, j]: The distance between x[i,] and x[centers[j],] | |
res.best <- list(tot.withinss = Inf) # the best result among <nstart> iterations | |
for (rep in 1:nstart) { | |
pr <- rep(1, n) # probability for sampling centers | |
for (i in 1:(k - 1)) { | |
centers[i] <- sample.int(n, 1, prob = pr) # Pick up the ith center | |
distances[, i] <- colSums((t(x) - x[centers[i], ])^2) # Compute (the square of) distances to the center | |
pr <- distances[cbind(1:n, max.col(-distances[, 1:i, drop = FALSE]))] # Compute probaiblity for the next sampling | |
} | |
centers[k] <- sample.int(n, 1, prob = pr) | |
## Perform k-means with the obtained centers | |
res <- kmeans(x, x[centers, ], iter.max = iter.max, nstart = 1, ...) | |
res$inicial.centers <- x[centers, ] | |
## Store the best result | |
if (res$tot.withinss < res.best$tot.withinss) { | |
res.best <- res | |
} | |
} | |
res.best | |
} |
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