- Suppose we have a classical k-means where iteratively each datapoint is assigned to its nearest center.
- After a certain time, suppose that we change the dataset by another similar dataset containing some additional classes, and I want to cluster it using only one pass, and allowing the number of clusters K to increase (when necessary). Actually this just a simple example to introduce my question.
My question: is there any statistical/probabilistic model or something that will allow us during the first phase to learn for example some values for each cluster, in order to predict if a new datapoint from the second phase should produce a new cluster or should be assigned to its nearest center.
Briefly, is there any useful statistics or probabilities that can be associated to each cluster in order to predict if a new datapoint is member of its nearest center or not. Maybe by using something like gaussian distribution for each cluster ... but how ...