IB Computer Science Topic A4.3.5 Learning Techniques Using the Association Rule Used in Large Data Sets Revision Notes (HL)

IB Computer Science Topic A4.3.5 Learning Techniques Using the Association Rule Used in Large Data Sets Revision Notes (HL) | RevisionDojo

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What Are Association Rules?

Association rules are if-then statements that reveal relationships between items in a data set.

Antecedent (If): The item or set of items that trigger the rule.
Consequent (Then): The item or set of items that result from the antecedent.

Example

In a supermarket, an association rule might be: If a customer buys bread and butter, Then they are likely to buy milk.

Key Metrics in Association Rule Learning

Support: The frequency of an itemset in the data set.
1. Formula: $$\text{Support}(A) = \frac{\text{Number of transactions containing } A}{\text{Total number of transactions}}$$
Confidence: The likelihood that the consequent occurs given the antecedent.
1. Formula: $$\text{Confidence}(A \Rightarrow B) = \frac{\text{Support}(A \cup B)}{\text{Support}(A)}$$
Lift: The strength of the rule compared to random chance.
1. Formula: $$\text{Lift}(A \Rightarrow B) = \frac{\text{Confidence}(A \Rightarrow B)}{\text{Support}(B)}$$

Note

A lift value greater than 1 indicates a strong positive association between the antecedent and consequent.

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What Are Association Rules?

Association rules are if-then statements that reveal relationships between items in a data set.

Antecedent (If): The item or set of items that trigger the rule.
Consequent (Then): The item or set of items that result from the antecedent.

Example

In a supermarket, an association rule might be: If a customer buys bread and butter, Then they are likely to buy milk.

Key Metrics in Association Rule Learning

Support: The frequency of an itemset in the data set.
1. Formula: $$\text{Support}(A) = \frac{\text{Number of transactions containing } A}{\text{Total number of transactions}}$$
Confidence: The likelihood that the consequent occurs given the antecedent.
1. Formula: $$\text{Confidence}(A \Rightarrow B) = \frac{\text{Support}(A \cup B)}{\text{Support}(A)}$$
Lift: The strength of the rule compared to random chance.
1. Formula: $$\text{Lift}(A \Rightarrow B) = \frac{\text{Confidence}(A \Rightarrow B)}{\text{Support}(B)}$$

Note

A lift value greater than 1 indicates a strong positive association between the antecedent and consequent.

Definition

Paywall

(on a website) an arrangement whereby access is restricted to users who have paid to subscribe to the site.

anim nostrud sit dolore minim proident quis fugiat velit et eiusmod nulla quis nulla mollit dolor sunt culpa aliqua

Duis aute irure dolor in reprehenderit

Note

Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam quis nostrud exercitation.

Excepteur sint occaecat cupidatat non proident

Tip

Lorem ipsum dolor sit amet, consectetur adipiscing elit.
Sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris.
Duis aute irure dolor in reprehenderit in voluptate velit esse cillum.

A1 Computer fundamentals4 subtopics

A2 Networks4 subtopics

A3 Databases4 subtopics

A4 Machine learning4 subtopics

B1 Computational thinking1 subtopic

B2 Programming5 subtopics

B3 Object-oriented programming2 subtopics

B4 Abstract data types (HL only)1 subtopic

IB Computer Science Topic A4.3.5: Learning Techniques Using the Association Rule Used in Large Data Sets Notes

A1 Computer fundamentals4 subtopics

A2 Networks4 subtopics

A3 Databases4 subtopics

A4 Machine learning4 subtopics

B1 Computational thinking1 subtopic

B2 Programming5 subtopics

B3 Object-oriented programming2 subtopics

B4 Abstract data types (HL only)1 subtopic

What Are Association Rules?

Key Metrics in Association Rule Learning

anim nostrud sit dolore minim proident quis fugiat velit et eiusmod nulla quis nulla mollit dolor sunt culpa aliqua

Duis aute irure dolor in reprehenderit

Excepteur sint occaecat cupidatat non proident

Unlock the rest of this chapter with a free account

What Are Association Rules?

Key Metrics in Association Rule Learning

anim nostrud sit dolore minim proident quis fugiat velit et eiusmod nulla quis nulla mollit dolor sunt culpa aliqua

Duis aute irure dolor in reprehenderit

Excepteur sint occaecat cupidatat non proident

Unlock the rest of this chapter with a free account