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# this code originally by Dan Putler, used with permission
# last modified 2012-12-06 by J. Fox
assignCluster <- function(clusterData, origData, clusterVec){
rowsDX <- row.names(clusterData)
rowsX <- row.names(origData)
clustAssign <- rep(NA, length(rowsX))
validData <- rowsX %in% rowsDX
clustAssign[validData] <- clusterVec
return(as.factor(clustAssign))
}
KMeans <- function (x, centers, iter.max=10, num.seeds=10) {
# fixed 15 Mar 05 by J. Fox
if(mode(x)=="numeric") x<-data.frame(new.x=x)
KM <- kmeans(x=x, centers=centers, iter.max=iter.max)
for(i in 2:num.seeds) {
newKM <- kmeans(x=x, centers=centers, iter.max=iter.max)
if(sum(newKM$withinss) < sum(KM$withinss)) {
KM <- newKM
}
}
KM$tot.withinss <- sum(KM$withinss)
xmean <- apply(x, 2, mean)
centers <- rbind(KM$centers, xmean)
bss1 <- as.matrix(dist(centers)^2)
KM$betweenss <- sum(as.vector(bss1[nrow(bss1),])*c(KM$size,0))
return(KM)
}
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