I have say 25 customers. Each customer has a number of users of our system, e.g. customer 1 has 45 users, customer 2 has 46 users... customer 25 has 1000 users.
I want to bin each customer into a bucket, where each bucket contains a roughly equal number of users. I know that I want 5 buckets in total.
(The buckets here represent servers, I want to apportion my clients to different servers where the total number of users per server is roughly equal, so as to prevent overloading the servers. 1 client has to be on the same server (i.e. can't split 1 client over 2 servers).
Any idea of suitable methods for apportioning customers to buckets? I thought some clustering methods might work (I tried kmeans using R), but I cant seem to find ways of stipulating that the total number of users in each cluster is roughly the same.
Here's my R code as an example of what I've done so far:
r <- data.frame(users=c(1000, 960, 920, 870, 850, 700, 600, 550, 520, 500, 420, 400, 390, 300, 210, 200, 160, 80, 70, 50, 49, 48, 47, 46, 45))
#Try kmeans clustering
fit <- kmeans(r, 5)
#get cluster means
aggregate(r, by=list(fit$cluster),FUN = mean)
#append cluster assignment
r <- data.frame(r,fit$cluster)
clusplot(r, fit$cluster, color=TRUE, shade=TRUE, labels=2, lines=0)
I don't know what the recommended solution for such 'constant sum sampling ' is. Here's my shot at it -- sort the items, convert to matrix where each column represents a sample, reverse every other row.
Here's the code:
set.seed(1024) r <- data.frame(users=c(1000, 960, 920, 870, 850, 700, 600, 550, 520, 500, 420, 400, 390, 300, 210, 200, 160, 80, 70, 50, 49, 48, 47, 46, 45)) a<- r$users #runif(n = 25, 100,400) #rnorm(25,100,100) # 1:25 #hist(a) df<- data.frame(id=1:25,x=a) # sort x<- df$id[order(df$x)] # convert to matrix #each column of this matrix represetns one sample xm<-matrix(x,ncol=5,byrow = T); xm oldsum<-apply(matrix(df$x,ncol=5,byrow = T), 2,sum) #flip alternate rows of this sorted matrix i= 1:nrow(xm) im=i[c(F,T)] xm[im,] xm[im,]<- rev(xm[im,]) # new matrix of indeices xm #hence the new matrix of values xm2<- matrix(a[c(xm)],ncol = 5, byrow = F) xm xm2 newsum<- (apply(xm2, 2,sum)) # improvement rbind(oldsum,newsum) barplot(rbind(oldsum,newsum)[1,]) barplot(rbind(oldsum,newsum)[2,]) # each column of following matrix represents one sample #(values are indices in original vector a) xm