Descriptive Data Mining from Power Point

In the context of association rule mining, what does the 'Lift Ratio' signify?

Which of the following scenarios would result in a lift ratio of 1 for an association rule?

Given an association rule 'If A, then C', a high confidence value indicates:

Using the data in Table 5.4, if the confidence of the rule {Diapers} -> {Beer} is 0.6, and the support for {Diapers, Beer} is 0.3, what is the support for {Diapers}?

What is the primary criteria for judging the value of an association rule?

In association rule mining, a high confidence score for a rule A -> B suggests which of the following?

Based on the provided association rules, which of the following rules has the highest confidence?

Based on Table 5.5, which rule has the highest lift ratio?

Which of the following is a limitation of using confidence as the sole measure for evaluating association rules?

If the Support for Soda is 6, the Support for Potato Chips is 4, and the Support for Soda & Potato Chips is 4, what is the confidence of the rule 'Soda -> Potato Chips'?

Which of the following is NOT a typical measure used to evaluate the strength or interestingness of association rules?

Given an association rule A -> B, what does the support of A represent?

Based on Table 5.5, what can be inferred about the association between 'Peanut Butter' and 'Milk'?

Walmart's discovery of the association rule 'If a customer purchases a Barbie doll, then a customer also purchases a candy bar' suggests what about association rule mining?

When would an association rule with high support and high confidence still NOT be considered 'useful'?

If the support for {A, B} is 0.2 and the support for A is 0.4, what is the confidence of the rule A -> B?

What is the primary goal of association rule mining in data analysis?

Beyond market basket analysis, in which of the following domains can association rules be effectively applied?

In an association rule, what is the role of the 'antecedent'?

Consider the transactions in Table 5.4. What is the support count for the itemset {jelly, soda}?

How do association rules primarily contribute to market basket analysis?

A supermarket notices that customers who purchase diapers frequently also buy baby wipes. In the context of association rules, 'diapers' would be the ______ and 'baby wipes' would be the ______.

What is a key distinction between association rule mining and classification?

When association rules indicate the 'likelihood' of items being purchased together, what type of relationship are they primarily describing?

In the context of market basket analysis, what does the 'antecedent' in an association rule represent?

Which of the following is the best definition of the 'support count' of an itemset in association rule mining?

How does association rule mining differ from traditional clustering techniques?

A retailer wants to identify products that are frequently purchased together to optimize shelf placement. Which data mining technique is most suitable for this purpose?

Given the association rule 'Soda -> Potato Chips' with a confidence of 66.7% and a lift ratio of 1.67, what does the lift ratio signify?

Consider a dataset of customer transactions. Which measure is used to determine the frequency with which an itemset appears in the dataset?

Which of the following adjustments would most directly improve the actionability of the association rule: 'If customer buys Barbie doll, then customer buys candy bar'?

Given a transaction database, how would you determine which product combinations have a support greater than a specified minimum support threshold?

In association rule mining, what does it mean to say an association rule has a lift value of less than 1?

According to the provided context, what is the ultimate criterion for judging the value of an association rule?

Based on Table 5.5, which of the following rules has the highest support for the antecedent?

Which of the following scenarios would benefit most from the application of association rule mining techniques?

Given the rules 'Soda -> Potato Chips' and 'Soda -> Milk' from Table 5.5, what can be inferred by comparing their confidence levels?

Considering the association rule 'If a customer purchases a Barbie doll, then a customer also purchases a candy bar,' what additional information would be most beneficial in determining the statistical significance of this rule?

Which scenario would suggest that an association rule, despite having high support and confidence, might not be particularly 'useful' according to the context?

Based on the association rules in Table 5.5, how does the presence of 'Fruit' in the antecedent affect the confidence of predicting 'Milk' as the consequent, compared to 'Soda' predicting 'Potato Chips'?

Which of the following statements best describes how confidence and lift ratio should be used together in evaluating association rules?

Why is it important to consider both confidence and lift ratio when assessing association rules?

In association rule mining, what could be a potential problem with relying solely on a high confidence value to identify valuable rules?

According to Table 5.5, which of the following rules would benefit most from further analysis to determine if the association is genuinely interesting, rather than coincidental?

How does the lift ratio help in discerning the actual strength of an association rule beyond what is indicated by its confidence?

If the confidence of a rule A -> B is 70%, and the lift ratio is 0.9, what can you infer about the relationship between A and B?

Consider two association rules: Rule 1 (Confidence: 60%, Lift: 1.2) and Rule 2 (Confidence: 80%, Lift: 0.9). Which rule is more likely to represent a genuinely interesting and non-trivial relationship?

A retailer discovers the association rule '{high-end coffee} -> {expensive pastries}' with high confidence but a lift ratio close to 1. What does this suggest?

Antecedent (Association Rules)

Consequent (Association Rules)

Support for A & C

Confidence (Association Rules)

Lift Ratio (Association Rules)

Actionability of Association Rules

Example of Association Rule

Characteristics of Useful Association Rule

K-Means Clustering

Association rules

Association rules application

Antecedent

Consequent

Support count

Shopping Cart Example

Jelly's support count.

Confidence in Association Rules

Lift Ratio

Example Confidence

Example Lift Ratio

Association Rule Example

Hy-Vee: Support for 'Bread'

Peanut Butter => Milk

Association Rule: Support

Soda -> Potato Chip Confidence

Fruit, Soda -> Milk Confidence

Milk -> Fruit, Soda Confidence

Milk -> Soda Confidence

Milk, Fruit -> Soda Confidence

Soda -> Milk Confidence

Soda -> Milk, Fruit Confidence

Judging Association Rules

Shopping-Cart Transactions

Example Shopping Cart Items

Association Rules Use

Support (Association Rules)

Lift Ratio Defined

High 'Lift Ratio'

Reliable Associations

Bread -> Jelly Confidence

Bread & Fruit -> Jelly

Support for A