Question

14. Association Rules Mining (ARM): (5 marks each) a. Explain using any example how ARM could be of enormous use in a business scenario.

Answer 1

Association Rules Mining(ARM):

The process of identifying an association between products/items is called association rule mining. Association Rule Mining is sometimes referred to as “Market Basket Analysis(as it consists of a large number of records on past transactions.)”. Data is collected using barcode scanners in most supermarkets. A single record lists all the items bought by a customer in one sale. Knowing which groups are inclined towards which set of items gives these shops the freedom to adjust the store layout and the store catalog to place the optimally concerning one another.

This rule has 2 parts: 1) an antecedent (if) and 2) a consequent (then)

An antecedent is something that’s found in data, and a consequent is an item that is found in combination with the

antecedent.

let's take an example, "If a customer buys bread, he’s 70% likely of buying jam.”

In the above association rule, bread is the antecedent and jam is the consequent.

Association rules are created by thoroughly analyzing data and looking for frequent if/then patterns. Then, depending on the following two parameters, the important relationships are observed:

1. Support: Support indicates how frequently the if/then relationship appears in the database.
2. Confidence: Confidence tells about the number of times these relationships have been found to be true.

Association Rule Mining primarily tries to find the rules that govern how or why such products/items are often bought together. To implement association rule mining, many algorithms have been developed. Apriori algorithm is one of the most popular and arguably the most efficient algorithms among them.

Apriori algorithm tells that all subsets of a frequent itemset to be frequent. Similarly, for any infrequent itemset, all its supersets must also be infrequent.

Suppose an X store’s retail transactions database includes the following data:

• Total number of transactions: 600,000
• Transactions containing bread: 7,500 (1.25 percent)
• Transactions containing jam: 60,000 (10 percent)
• Transactions containing both bread and jam: 6,000 (1.0 percent)

we can conclude that if there was no relation between bread and jam (that is, they were statistically independent), then we would have got only 10% of bread purchasers to buy jam too.

From the above data, 80% (6000/7500) of the people who buy bread also buy jam.This was unexpected probability. This factor of increase is known as Lift( ratio of the observed frequency of co-occurrence of our items and the expected frequency.)