# Determine number of clusters
wss <- (nrow(input_output_10000_2)-1)*sum(apply(input_output_10000_2,2,var))
for (i in 2:15) wss[i] <- sum(kmeans(input_output_10000_2, centers=i)$withinss)
plot(1:15, wss, type="b", xlab="Number of Clusters", ylab="Within groups sum of squares") 

# K-Means Cluster Analysis
fit <- kmeans(input_output_10000_2, 3) # 3 cluster solution
# get cluster means
aggregate(input_output_10000_2,by=list(fit$cluster),FUN=mean)
# append cluster assignment
input_output_10000_2 <- data.frame(input_output_10000_2, fit$cluster)