What is Association Analysis? Where is it used?
Association analysis uses a set of transactions to discover rules that indicate the likely occurrence of an item based on the occurrences of other items in the transaction.
The technique of association rules is widely used for retail basket analysis. It can also be used for classification by using rules with class labels on the right-hand side. It is even used for outlier detection with rules indicating infrequent/abnormal association.
Association analysis also helps us to identify cross-selling opportunities.
for example, we can use the rules resulting from the analysis to place associated products together in a catalog, in the supermarket, or the Webshop, or apply them when targeting a marketing campaign for product B at customers who have already purchased product A.
Association rules are given in the form below:
A=>B[Support, Confidence]
The part before (\=>) is referred to as if (Antecedent).
The part after (\=>) is referred to as then (Consequent).
Where A and B are sets of items in the transaction data, a and B are disjoint sets.
Computer=>Anti−virusSoftware[Support=20%,confidence=60%] Above rule says:
20% of transactions show Anti-virus software is bought with the purchase of a Computer.
60% of customers who purchase Anti-virus software bought with the purchase of a Computer.
An example of Association Rules * Assume there are 100 customers:
10 of them bought milk, 8 bought butter, and 6 bought both of them 2.
bought milk => bought butter.
support = P(Milk & Butter) = 6/100 = 0.06
confidence = support/P(Butter) = 0.06/0.08 = 0.75
lift = confidence/P(Milk) = 0.75/0.10 = 7.5