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DBScan Clustering for test dataset from learnt trained model

John_De_JongJohn_De_Jong Member Posts: 10 Contributor II
edited November 2018 in Help
From the code, i can see that DBScan maps every instance in training set to a cluster based on density. But wanted to see how given a new data/test set it can use the clustering model to map to existing cluster? In KMeans atleast we have centroids that are learnt and we use distance measure to find closest distance of data to the learnt clusters, how does DBScan do this?
I wanted to compare EM, DBScan and KMeans on a given problem(train, test) and see how they map.

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