I have a database of tags given by users to the product. For example
user; product; tag 1; A; Tag1 1; A; Tag2 2; A; Tag1 2; B; Tag1 .. ..
I am trying to cluster tags which are given together to any product. At the end I want to visualize them like PCA plot where I can see clusters of tags which are 'closer' (tendency of user to assign these tags together) to each other. So far I can think of applying t-SNE or simply PCA to get some kind of clustering with available tags.
For this purpose, I made frequency matrix like following
first_tag; second_tag; occurrence Tag1; Tag2; 2393 Tag1; Tag3; 38 Tag2; Tag3; 8393 .. ..
I am stuck here. I don't know how to proceed with clustering. I simply tried visualizing it with
networkx library of
python where I made edges with 'first_tag' and 'second_tag' and 'occurrence' as its weight. But it was futile exercise, I couldn't get anything out of it. I thought of using
sklearn.decomposition PCA but I am struggling to convert this data-set into proper matrix which can be fed to such algorithms. I can always make
n x n matrix by making rows and columns equal to number of tags but it was causing very slow and sometimes out of memory errors. Any other elegant solution for this?