I'm trying to measure association in my basket based on transactions. Let's imagine I have 5 products. Then, I calculate the number of client who bought product A and B, A and C, B and C and so on.
Total columns is the number of clients who bought the product.
I calculate penetration rate (or support when we use association rule) = number of client who bought product A and B / number of client (in my example 5)
Then, I calculate kind of confidence = number of client who bought product A and B / number of client who bought product A
Finally, I calculate my lift = confidence / support.
Results are here:
- Why my lift value are all the same by line?
=> I know why. Because support = p(a) * p(b) and confidence = p(a) * p(b) / p(a) so lift = 1/p(a)
- I need to take into account the volume effect because the products highlighted in my analysis are always those that people buy the most, so what techniques do you know for this problem?