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Association Rules Mining

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Business Analytics

Lecture # 11

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TOPICS to be COVERED

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Evaluating Association Rules

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Apriori Algorithm

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ASSOCIATION RULE

In marketing, analysing consumer behaviour can lead to insights regarding the placement and promotion of products.

Specifically, marketers are interested in examining transaction data on customer purchases to identify the products commonly purchased together

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Association rules, which convey the likelihood of certain items being purchased together.
Although association rules are an important tool in market basket analysis,
They are also applicable to disciplines other than marketing.
For example, association rules can assist medical researchers in understanding which treatments have been commonly prescribed to certain patient symptoms (and the resulting effects).

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Table 4.4 contains a small sample of data in which each transaction comprises the items purchased by a shopper in a single visit to a Hy-Vee.
An example of an association rule from this data would be “if {bread, jelly}, then {peanut butter},” meaning that “if a transaction includes bread and jelly, then it also includes peanut butter.”
The collection of items (or item set) corresponding to the if portion of the rule, {bread, jelly}, is called the antecedent.
The item set corresponding to the then portion of the rule, {peanut butter}, is called the consequent.

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To formalize the notion of “frequent,” we define the support count of an item set as the number of transactions in the data that include that item set. In Table 4.4, the support count of {bread, jelly} is 4.

The potential impact of an association rule is often governed by the number of transactions it may affect, which is measured by computing the support count of the item set consisting of the union of its antecedent and consequent.

Investigating the rule “if {bread, jelly}, then {peanut butter}” from Table 4.4, we see the support count of {bread, jelly, peanut butter} is 2.

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Association Rules

Confidence: Helps identify reliable association rules

Lift ratio: Measure to evaluate the efficiency of a rule

For the data in Table , the rule “if {bread, jelly}, then {peanut butter}” has confidence = 2/4 = 0.5 and a lift ratio = 0.5/(4/10) = 1.25

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This measure of confidence can be viewed as the conditional probability of the consequent item set occurs given that the antecedent item set occurs.
A high value of confidence suggests a rule in which the consequent is frequently true when the antecedent is true, but a high value of confidence can be misleading.

For example, if the support of the consequent is high—that is, the item set corresponding to the then part is very frequent—then the confidence of the association rule could be high even if there is little or no association between the items.

A lift ratio greater than 1 suggests that there is some usefulness to the rule and that it is better at identifying cases when the consequent occurs than no rule at all.

For the data in Table 4.4, the rule “if {bread, jelly}, then {peanut butter}” has confidence = 2/4 = 0.5 and a lift ratio = 0.5/(4/10) = 1.25.

In other words, identifying a customer who purchased both bread and jelly as one who also purchased peanut butter is 25 percent better than just guessing that a random customer purchased peanut butter.

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To help identify reliable association rules, we define the measure of confidence of a rule, which is computed as:

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Thank You !

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