# Research in random forest algorithms able to switch data sets

I'm curious as to whether research been done into random forests that combine unsupervised with supervised learning in a way allowing a single algorithm to find patterns in, and work with, multiple different data sets. I have googled every possible way to find research on this, and have come up empty. Can anyone point me in the right direction?

Semi-Supervised Learning

The combination of unsupervised learning and supervised learning is referred to as semi-supervised learning, which is the concept that I believe you are searching for.

Label propagation is often cited when outlining the heuristics of semi-supervised learning. The essence is to employ clustering, but to use a tiny set of known cases in order to derive (or propogate) the labels of the clusters. Hence one is able to use a small set of labeled cases to classify a much larger set of unsupervised data.

Here are some references:

Hope this helps!

• This is genius! Thank you so much. I am new to data science (as a university student) and I had no idea where to start to learn for myself. Those links are perfect. – Jessiah Burgess Dec 31 '15 at 0:25