Suggestion for a better way to organize data to generate frequent item-sets?
If the size is reasonable (i.e. not too many documents and not too many words in a document), you could try to build a map for each possible itemset, for instance like this:

// Assuming data is an array of size N containing all the documents
// clusters is a map associating each itemset with a set of documents
for i=0 to N-1
  for j=i+1 to N-1
    group = overlap(data[i], data[j])
    add data[i] to the set clusters[group]
    add data[j] to the set clusters[group]


An alternative version if the number of different values and size of the sets allow it and/or if it's possible to precompute the itemsets of interest:

for i=0 to N-1
  for every subset S of data[i]
    add data[i] to the set clusters[S] 


(adapted from https://datascience.stackexchange.com/a/60609/64377)