Automated K-Means Clustering and R Implementation Kim, Sung-Soo;
The crucial problems of K-means clustering are deciding the number of clusters and initial centroids of clusters. Hence, the steps of K-means clustering are generally consisted of two-stage clustering procedure. The first stage is to run hierarchical clusters to obtain the number of clusters and cluster centroids and second stage is to run nonhierarchical K-means clustering using the results of first stage. Here we provide automated K-means clustering procedure to be useful to obtain initial centroids of clusters which can also be useful for large data sets, and provide software program implemented using R.
K-means clustering;Ward's method;Mojena's stopping rule;model-based clustering;BIC(Bayesian Information Criteria);automated K-means clustering;