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Comments on the K-Means Method

  • Strength: Efficient: O(tkn), where n is # objects, k is # clusters, and t is # iterations. Normally, k, t << n.
      • Comparing: 

\[PAM: O(k(n-k)^{2}), CLARA: O(ks^{2} + k(n-k))\]

  • Comment: Often terminates at a local optimal
  • Weakness
    • Applicable only to objects in a continuous n-dimensional space
      • Using the k-modes method for categorical data
      • In comparison, k-medoids can be applied to a wide range of data
    • Need to specify k, the number of clusters, in advance (there are ways to automatically determine the best k (see Hastie et al., 2009)
    • Sensitive to noisy data and outliers
    • Not suitable to discover clusters with non-convex shapes

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