I want to use k-means clustering on my dataset to capture the similarity based on two attributes for two groups. I am looking to split my data first into training and testing, and then find clusters based on the training data and test the same on the new data.
The only concern I have right now is that during sampling, the distribution of data should not be biased. I have data for two groups 0 and 1, for which I want to split, and validate whether the two attributes for new data fall into group 0 or 1.
What are the points that I need to take care in mind while sampling the data? I assume it should not be random sampling, and needs to be done properly, to ensure that skewness is not present in the data, else the entire objective of clustering will fail.