I have a labeled dataset with data under 3 classes. Some of the instances in the dataset can be similar although they may belong to different classes. So I tried clustering the dataset without considering the labels. Once clustering was done, the already known labels were assigned to instances in each cluster.This way I will have K clusters and each cluster has instances belonging to different classes. I wanted to know if it makes sense to apply supervised learning algorithms like LDA, QDA, SVM,kNN etc. on each of these clusters separately and identify the best performing classifier for each cluster?
The data represents the sweat gland activity of a subject under 3 levels of physical activity say less intense, moderately intense, very intense. As the sweat gland functioning is different for different individuals (for eg., if the sweat gland density of a person is more than another, his sweat levels during a low-intensity exercise might be similar to the very intense level activity of the other who has lesser sweat gland density).In such a case, the data represents similar values but come under two labels low and high.
Ultimately, the objective is to classify different physical activity levels(Low, moderate or high) based on the sweat gland activity.