I am working on a classification task. The dataset is a UCI data set about machine learning with 200 observations and 2 classes.
Part of my model includes the following preprocessing steps:
- remove missing values
- normalize between 0 and 1
- remove outlier
- remove trend from data
I would like to use a clustering method to remove noisy data points. The question is, at which step should this happen?