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Automated K-Means Clustering and R Implementation

자동화 K-평균 군집방법 및 R 구현

  • Kim, Sung-Soo (Department of Information Statistics, Korea National Open University)
  • 김성수 (한국방송통신대학교 정보통계학과)
  • Published : 2009.08.31

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.

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Cited by

  1. A Variable Selection Procedure for K-Means Clustering vol.25, pp.3, 2012, https://doi.org/10.5351/KJAS.2012.25.3.471
  2. Variable Selection and Outlier Detection for Automated K-means Clustering vol.22, pp.1, 2015, https://doi.org/10.5351/CSAM.2015.22.1.055