R/fpca.nonscore.cluster.R

In FusedPCA: Community Detection via Fused Principal Component Analysis

fpca.nonscore.cluster <- function(obj, K = 2){
	
	if(K == 2){
		
		n = dim(obj)[1]
		n.loc = dim(obj)[2]
		cluster.list = matrix(0, nrow = n, ncol = n.loc)
		
		for(i in 1:n.loc){
			temp1 = unique(obj[, i])
			if(length(temp1) >= K){
				k.means = kmeans(obj[, i], centers = K)
				cluster.list[, i] = k.means$cluster
			}
		}
	}
	
	
	if(K > 2){
		
		n = dim(obj)[1]
		n.loc = dim(obj)[3]
		cluster.list = matrix(0, nrow = n, ncol = n.loc)
		
		for(i in 1: n.loc){
			temp1 = unique(obj[, , i], margin = 2)
			if(dim(temp1)[1] >= K){
				k.means = kmeans(obj[, , i], centers = K)
				cluster.list[, i] = k.means$cluster
			}
		}
	}
	
	return(cluster.list)
	
}