• The Korean Journal of Applied Statistics
• /
• v.22 no.4
• /
• pp.723-733
• /
• 2009

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.

• The Korean Journal of Applied Statistics
• /
• v.25 no.3
• /
• pp.471-483
• /
• 2012

One of the most important problems in cluster analysis is the selection of variables that truly define cluster structure, while eliminating noisy variables that mask such structure. Brusco and Cradit (2001) present VS-KM(variable-selection heuristic for K-means clustering) procedure for selecting true variables for K-means clustering based on adjusted Rand index. This procedure starts with the fixed number of clusters in K-means and adds variables sequentially based on an adjusted Rand index. This paper presents an updated procedure combining the VS-KM with the automated K-means procedure provided by Kim (2009). This automated variable selection procedure for K-means clustering calculates the cluster number and initial cluster center whenever new variable is added and adds a variable based on adjusted Rand index. Simulation result indicates that the proposed procedure is very effective at selecting true variables and at eliminating noisy variables. Implemented program using R can be obtained on the website "http://faculty.knou.ac.kr/sskim/nvarkm.r and vnvarkm.r".