I've written Naive Bayesian classifiers before, they work wonderfully. But I'd like a classifier which will learn like a Bayesian classifier and identify new classifications when a new cluster emerges.
Lets presume I have sensor data for some sort of machinery. I have training data for different conditions - too hot, to cold, misfires, etc. Now lets assume some new condition occurs - say a misalignment, or say over revving.
What techniques would allow for that new cluster of data to build a new classification? Naive Bayes+K-Means?