DBSCAN quality check using python
I have a dataset of 781 instances where two main categories exist with numerical values. Such that there is one column called ‘ΔR(mm)’. the dataset has main two categories according to the values of this column (the values with negative sign and the values of positive sign). When I run the DBSCAN algorithm in python I got 5 clusters each of mixed values (negative and positive) with varied number of instances per each cluster. E.g., 135,27, 423, 31, 145 instances in clusters 1, 2,3, 4 and 5 respectively. I set the hyperparameters eps = 1400 , and min_samples = 10 to reduce the number of noise to 11 points. So, I have two questions according to these givens:
Which clustering technique and distance measure should I choose if I have a dataset with binary, continuous variables, and different distributions?
I am performing clustering in Python to find groups in a dataset.