I have applied clustering algorithms like k-mean, k-medoid and DBSCAN on my patients dataset. For each algorithm RapidMiner generated a clustered model (centroid table and graphs etc) and a clustered set (shows which examples are part of which cluster). Now I want some way that when a new patient come i want to assign him a cluster based on previous trained model. I am confused of the way to do this… Is it something like that (I may be wrong)?
For each attribute value of new patient - that attribute value from centroid table summing all the differences of attributes of patient and taking average.
Then assign him cluster whose average is minimum with respect to that patient.
If this the right way then how will I re-cluster, i.e when a new patient comes our algorithm is assigning him cluster, that's mean. centroid moves and then I have to re-cluster with each record insertion. How to handle this in my scenario?