Market Basket Analysis (MBA)

This is typically used for frequently bought items mining. In very simple terms, this process includes looking at the customers’ past behaviour and building associations between the different items.

Usage of this can be seen in every domain. For example, on an eCommerce portal you see “customers who bought this also bought this” or “frequently bought together”.

Similarly in the retail world, you will see examples of items which are “bundled or placed together” based on what is frequently bought together.

In the social media you would have seen “friends and connections recommendation”. How about Netflix’s and You tube’s “videos and movies recommendation”. All of these can be achieved using the MBA.

Support:

The support, Supp() of an itemset is defined as the proportion of transactions in the data set which contain the item set.

Support Score Supp(A) = (Count of product A in N transaction)/ Total Transactions (N)

It helps on focus on high volume products and products combinations. Generally, we deal with millions of combinations and it helps us focus on high support items.

Confidence:
Confidence is defined as the conditional probability that a transaction containing the

LHS will also contain the RHS.
Confidence (LHS-->RHS) = P(RHS | LHS) = P(RHS∩LHS)/P(LHS) = Support(RHS, LHS)/Support(LHS)

Lift:

Lift is defined as Lift (A,B) and is given as the ratio of the observed support to that expected if A and B were independent

Lift (A,B) = Supp(AUB)/Supp(A) X Supp(B) = P(RHS/LHS)/P(RHS)