What are the weaknesses of K-Mean Clustering?
Similar to other algorithm, K-mean clustering has many weaknesses:
When the numbers of data are not so many, initial grouping will determine the cluster significantly.
The number of cluster, K, must be determined before hand.
We never know the real cluster, using the same data, if it is inputted in a different way may produce
different cluster if the number of data is a few.
We never know which attribute contributes more to the grouping process since we assume that each
attribute has the same weight.
One way to overcome those weaknesses is to use K-mean clustering only if there are available many data