[synclust.git] / R / clustering.R

#main function (choice between kmeans and hierarchical clustering)
getClusters = function(distances, method, K)
{
	clusts = c()
	if (method=="KM")
	{
		nstart = 10 #number of kmeans random restarts
		maxiter = 100 #maximum iterations count in each km run
		clusts = .Call("kmeansWithDistances", distances, K, nstart, maxiter)
	}
	else if (method=="HC")
	{
		#simple hierarchical clustering using ECT distances
		hct = hclust(as.dist(distances),method="ward.D")
		clusts = cutree(hct, K)
	}
	return (clusts)
}

# renumbering step (post-processing after clustering)
reordering = function(clusts)
{
	newCl = clusts
	maxInd = max(clusts)
	counter = 1
	for (i in 1:maxInd)
	{
		if (sum(clusts == i) > 0)
		{
			newCl[clusts == i] = counter
			counter = counter + 1
		}
	}
	return (newCl)
}
Commit	Line	Data
15d1825d BA	1	#main function (choice between kmeans and hierarchical clustering)
	2	getClusters = function(distances, method, K)
	3	{
	4	clusts = c()
	5	if (method=="KM")
	6	{
	7	nstart = 10 #number of kmeans random restarts
	8	maxiter = 100 #maximum iterations count in each km run
	9	clusts = .Call("kmeansWithDistances", distances, K, nstart, maxiter)
	10	}
	11	else if (method=="HC")
	12	{
	13	#simple hierarchical clustering using ECT distances
	14	hct = hclust(as.dist(distances),method="ward.D")
	15	clusts = cutree(hct, K)
	16	}
	17	return (clusts)
	18	}
	19
	20	# renumbering step (post-processing after clustering)
	21	reordering = function(clusts)
	22	{
	23	newCl = clusts
	24	maxInd = max(clusts)
	25	counter = 1
	26	for (i in 1:maxInd)
	27	{
	28	if (sum(clusts == i) > 0)
	29	{
	30	newCl[clusts == i] = counter
	31	counter = counter + 1
	32	}
	33	}
	34	return (newCl)
	35	}