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http://dx.doi.org/10.5351/KJAS.2009.22.4.723

Automated K-Means Clustering and R Implementation  

Kim, Sung-Soo (Department of Information Statistics, Korea National Open University)
Publication Information
The Korean Journal of Applied Statistics / v.22, no.4, 2009 , pp. 723-733 More about this Journal
Abstract
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.
Keywords
K-means clustering; Ward's method; Mojena's stopping rule; model-based clustering; BIC(Bayesian Information Criteria); automated K-means clustering;
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Times Cited By KSCI : 1  (Citation Analysis)
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