K-Means Advantages:
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If variables are huge, then K-Means most of the times computationally faster than hierarchical clustering, if we keep k smalls.
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K-Means produce tighter clusters than hierarchical clustering, especially if the clusters are globular.
K-Means Disadvantages:
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Difficult to predict K-Value.
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With global cluster, it didn’t work well.
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Different initial partitions can result in different final clusters.
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It does not work well with clusters (in the original data) of Different size and Different density