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Automated K-Means Clustering and R Implementation
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 Title & Authors
Automated K-Means Clustering and R Implementation
Kim, Sung-Soo;
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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;
 Cited by
A Variable Selection Procedure for K-Means Clustering,Kim, Sung-Soo;

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