In K-means clustering, you can specify an nstart=i parameter, which performs the algorithm i times (i.e. selects the initial k random centroids i times) sand reports the best answer only. If I perform this specifying nstart=10^6, then I get the same (presumably the best) results everytime I run kmeans for a given k.
However, in EM clustering, I am not sure which is the way to do this, if there is a way at all. I am using the EMCluster package in R, and when initializing the model for assigning clusters/classes, I am using the exhaust.EM function, with a
min.n.iter=10^9. This still gives me a different answer everytime: some datapoints will belong to one cluster in one execution, and they will be excluded from that cluster the next.
Is there an equivalent to
nstart from k-means for EM clustering?