I want to know if the following is a valid approach to create labels, if I have measurements under some conditions, and the conditions are similar but never exactly the same.
This doesn't correspond exactly to my real problem but for convenience lets say I have two WiFi Networks A and B. I want to know under which conditions A or B performs better.
My first step is to transmit Data over A and over B. I measure the network conditions and the time it takes to transmit the Data. The problem is that the captured conditions are never exactly the same. Hence I can't directly assign a label (e.g. "under this conditions A or B is better").
So I would perform a k-means clustering of the conditions and group similar conditions together. For each point in a cluster I lookup weather the transmission was performed with A or B and compare e.g. the medians of the transmission times.
Now I have a label (A better or B better) for each cluster center and can train a supervised model to generalize.
Is this a valid or common approach in those situations?