title: Using frequent itemset mining to build association rules?
body: <p>I am new to this area as well as the terminology so please feel free to suggest if I go wrong somewhere. I have two datasets like this:</p>
  
  <h3>Dataset 1:</h3>
  
  <pre><code>A B C 0 E
  A 0 C 0 0
  A 0 C D E
  A 0 C 0 E
  </code></pre>
  
  <p>The way I interpret this is at some point in time, (A,B,C,E) occurred together and so did (A,C), (A,C,D,E) etc.</p>
  
  <h3>Dataset 2:</h3>
  
  <pre><code>5A 1B 5C  0 2E
  4A  0 5C  0  0
  2A  0 1C 4D 4E
  3A  0 4C  0 3E
  </code></pre>
  
  <p>The way I interpret this is at some point in time, 5 occurrences of A, 1 occurrence of B, 5 occurrences of C and 2 occurrences of E happened and so on.</p>
  
  <p>I am trying to find what items occur together and if possible, also find out the cause and effect for this. For this, I am not understanding how to go about using both the datasets (or if one is enough). It would be good to have a good tutorial on this but my primary question is which dataset to utilize and how to proceed in (i) building a frequent itemset and (ii) building association rules between them.</p>
  
  <p>Can someone point me to a practical tutorials/examples (preferably in Python) or at least explain in brief words on how to approach this problem?</p>