What are the potential issues with the K-means clustering method?
- It cannot handle non-spherical clusters
- It does not work well with non-numeric data
- It is sensitive to outliers
- All the options
The K-means clustering method can have several issues: it doesn't work well with non-numeric data, it's sensitive to outliers (since outliers can significantly move the cluster centroids), and it has difficulty handling clusters that are non-spherical or have varying sizes and densities.
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