• Title/Summary/Keyword: 자동화 K-평균 군집분석

Search Result 1, Processing Time 0.035 seconds

Automated K-Means Clustering and R Implementation (자동화 K-평균 군집방법 및 R 구현)

  • Kim, Sung-Soo
    • 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.