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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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