Discovering groupings of descriptive tags from media Some media websites have a collection of descriptive tags paired to every image or video, which are intended to help users filter their searches. Such data are observational, so I would limit an analysis to exploring the properties of a (large) sample without inferring causal effects. Rather, I am interested in hypothesis generation from an exploratory data analysis.
For each tag we can create an indicator function $I_{x\in A_i}(x)$ for the $i$th tag. I am interested in learning which tags tend to co-occur, and which tags to tend to not co-occur. Observed groupings of tags might suggest, but not rigorously demonstrate, possible groupings of content preferences.
I could calculate joint frequency probabilities and compare them to the marginals, or perform frequent pattern mining, or one of various clustering algorithms. From a technical standpoint, I essentially have a collection of one-hot vectors. I am wondering what is a suitable approach for finding groupings with such data based on co-occurrence.

If you are after visualizing the relationships, then you might use some network visualization algorithms.
I did this once with the tags of cross validated (Disclaimer: I am not an expert at this). I made a matrix that keeps track of the number of times of times that the different tags are appearing together. Then I used that matrix as

*

*a matrix describing the edges in a network graph

*a correlation matrix in PCA and

*3 after some arbitrary trial and error with transformations, as a dissimilarity matrix in an hierarchical clustering method


