I researched about k-means and these are what I got: k-means is one of the simplest algorithm which uses unsupervised learning method to solve known clustering issues. It works really well with large datasets.
However, there are also drawbacks of K-Means which are:
- Strong sensitivity to outliers and noise
- Doesn't work well with non-circular cluster shape -- number of cluster and initial seed value need to be specified beforehand
- Low capability to pass the local optimum.
Is there anything great about k-means, because it seems that the drawbacks are beyond the good things about k-means.
Please teach me.