In some clustering algorithm, ex:
K-Means cluster, it is very sensitive with outliers, so we need to remove outliers before aplly
K-Means, or it will be bad clustering. So :
- How can we know some points are outliers if we can not plot it ( high dimension data ) ?
- how can we know its
K-Meansmodel is good or bad ? Because it is unsupervised learning, so we can not calculate accuracy rate ( something likes F1 score ,... ). Or do we have any method to know an unsupervised learning model is good or bad ?