What are pros and cons of K-means?

K-Means Advantages:

  1. If variables are huge, then K-Means most of the times computationally faster than hierarchical clustering, if we keep k smalls.

  2. K-Means produce tighter clusters than hierarchical clustering, especially if the clusters are globular.

K-Means Disadvantages:

  1. Difficult to predict K-Value.

  2. With global cluster, it didn’t work well.

  3. Different initial partitions can result in different final clusters.

  4. It does not work well with clusters (in the original data) of Different size and Different density