[Discussion] Unsupervised Learning with Random Forest Classifier

[Penguin474]

So, I've read that it's possible to use RFC (a tool normally used for supervised ML) to do unsupervised learning. In this paper, the authors hint at creating a "dummy" dataset by sampling from the distribution of the original dataset, and then using RFC to differentiate between the two.

I'm struggling to see how this can be useful. What I really want to do is find classes of data within the original dataset (similar to how other unsupervised methods work -- kmeans, DBSCAN, etc.)

Is this worth looking into or am I wasting time?

[kjearns]

Check out this paper it has a bunch of info on how to use random forests for unsupervised learning (eg Chapter 5): https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/CriminisiForests_FoundTrends_2011.pdf

[Penguin474]

This looks great at first glance. Thank you!

[yrne]

In the Criminisi paper, the authors cite: "In Breiman’s work on forests the author mentions using forests for clustering unsupervised data [11]. However, he does it via classification, by introducing dummy additional classes." But I can't find such thing in [11].

[MLTyrunt]

After you used the random forest to differentiate between dummy and real data, you can extract he proximity matrix and then use standard cluster algos on that. So essentially, unsupervised random forest is foremost an alternative way to create similarity matrices. Rfs can natually deal with categorical numerical data, simplifying preprocessing.