I want to use a neural network to find correlated columns in a .csv file and give them as a output. The input .csv file has multiple columns with 0 and 1 ( like Booleans) in it. The file got the assignment from people to interests in it.
Example .csv input:
UserID History Math Physics Art Music ...
User1 0 1 1 0 0 ...
User2 0 0 0 1 1 ...
User3 0 1 1 1 1 ...
User4 1 0 1 1 0 ...
...
The output should be in this case something like: {math,physics}, {art,music}, {history,physics,art} - I exclude here {math,physics,art,music} because in a step afterwards i want to exclude (at least some) which can be created through the combination of others.
At the moment I have a problem that i don´t know which type of neural network could complete this task. How can I solve this problem?
So the important thing, that a column can have more than one column it correlates to - so its not like simple k-means clustering (as far as I understand it).
