LLFinit1 = list()
require(MASS) #Moore-Penrose generalized inverse of matrix
+ require(mclust) # K-means with selection of K
for(repet in 1:20)
{
- clusters = hclust(dist(y)) #default distance : euclidean
- #cutree retourne les indices (? quel cluster indiv_i appartient) d'un clustering hierarchique
- clusterCut = cutree(clusters,k)
- Zinit1[,repet] = clusterCut
+ clusters = Mclust(matrix(c(X,Y),nrow=n),k) #default distance : euclidean
+ Zinit1[,repet] = clusters$classification
for(r in 1:k)
{