The requirements of the project is to cluster the dataset (using k-means) and then remove the outliers (using MAD) from each of the cluster.
However, I don't feel that it make sense to do that. I think outliers should be removed from the dataset first and then do the clustering.
I'm really new to k-means and machine learning in general. I would really appreciate suggestions. Thanks in advance!
EDIT1: Answering @Tim as to why outliers should be removed:
There are actually 2 process.
running the k-means,
removing the outliers from each cluster