Association Rule Mining: Master Market Basket Analysis with Apriori

Question 1

Which of the following is NOT a measure used to assess the strength of association rules?

Accepted Answer

Entropy

Answer

Support

Answer

Confidence

Answer

Lift

Question 2

What is a key strength of the Apriori algorithm?

Accepted Answer

Efficiently handling large and complex datasets

Answer

Assuming binary relationships between items

Answer

Generating redundant rules

Answer

Being hindered by scarce data for rule generation

Question 3

In the Apriori algorithm, what is the significance of the downward closure property?

Accepted Answer

It helps identify infrequent itemsets during candidate generation and eliminates them.

Answer

It ensures that all possible itemsets are generated.

Answer

It combines frequent itemsets to form larger sets.

Answer

It computes the support of each itemset.

Question 4

What is the primary objective of association rule mining?

Accepted Answer

To uncover patterns and relationships within data.

Answer

To predict future occurrences.

Answer

To categorize data into distinct groups.

Answer

To decrease the dimensionality of data.

Question 5

What is the distinction between strong and weak association rules?

Accepted Answer

Strong rules have high support and confidence, while weak rules have low support and low confidence.

Answer

Strong rules have low support and high confidence, while weak rules have high support and low confidence.

Answer

There is no distinction between strong and weak association rules.

Answer

Strong rules have high confidence and low support, while weak rules have low confidence and high support.

Question 6

What is the primary application of the AprioriTid algorithm?

Accepted Answer

Mining association rules in transactional databases.

Answer

Mining association rules in time-series data.

Answer

Mining association rules in graph data.

Answer

Mining association rules in high-dimensional data.

Question 7

In the Apriori algorithm, what is the purpose of setting a support threshold?

Accepted Answer

To identify itemsets that occur frequently enough to be considered for association rule generation

Answer

To determine the strength of association between itemsets

Answer

To limit the number of candidate itemsets generated

Answer

To ensure that all possible association rules are generated

Question 8

Which step in the Apriori algorithm involves removing infrequent itemsets?

Accepted Answer

Pruning

Answer

Generation of candidate itemsets

Answer

Support counting for candidate itemsets

Answer

Generation of association rules

Question 9

Which metric measures the probability that an item will be present in a transaction given that another item is present?

Accepted Answer

Confidence

Answer

Support

Answer

Lift

Answer

Conviction

Question 10

What is a characteristic of an association rule that indicates a strong relationship between items?

Accepted Answer

High support and high confidence

Answer

High support and low confidence

Answer

Low support and high confidence

Answer

Low support and low confidence

Question 11

Which of the following is NOT an advantage of using association rule mining for data analysis?

Accepted Answer

Predicting future events

Answer

Discovering hidden patterns and relationships in data

Answer

Providing insights into customer behavior

Answer

Detecting anomalies in data

Question 12

What is the key difference between closed and maximal frequent itemsets?

Accepted Answer

Maximal itemsets are subsets of closed itemsets

Answer

Closed itemsets contain more items than maximal itemsets

Answer

Closed itemsets have higher support than maximal itemsets

Answer

Maximal itemsets have higher confidence than closed itemsets

Question 13

Which data structure is typically used to store candidate itemsets in the Apriori algorithm?

Accepted Answer

Hash table

Answer

Stack

Answer

Queue

Answer

Graph

Question 14

Which fundamental concept in Association Rule Mining refers to a collection of items purchased together?

Accepted Answer

Transaction

Answer

Support

Answer

Confidence

Answer

Lift

Question 15

In the Apriori algorithm, what is the initial step that generates candidate itemsets of length 1?

Accepted Answer

Generate L1

Answer

Compute support

Answer

Generate candidate itemsets

Answer

Prune infrequent itemsets

Question 16

Which measure quantifies the prevalence of an itemset in a dataset, indicating how frequently it appears?

Accepted Answer

Support

Answer

Confidence

Answer

Lift

Answer

Rule strength

Question 17

Which technique is commonly used to handle continuous-valued attributes in Association Rule Mining, converting them into discrete values?

Accepted Answer

Binning

Answer

Smoothing

Answer

Clustering

Answer

Normalization

Question 18

What is the primary reason for pruning infrequent itemsets in the Apriori algorithm?

Accepted Answer

Reduce computational complexity and improve efficiency

Answer

Improve accuracy and enhance interpretability

Answer

Enhance interpretability and increase support

Answer

Increase support and improve accuracy

Question 19

What is the fundamental distinction between support and confidence in Association Rule Mining?

Accepted Answer

Support measures the prevalence of an itemset, while confidence measures the strength of an association rule

Answer

Confidence measures the prevalence of an itemset, while support measures the strength of an association rule

Answer

Both support and confidence measure the strength of an association rule

Answer

Both support and confidence measure the prevalence of an itemset

Question 20

In market basket analysis, which of these is NOT a commonly used metric for assessing the quality of association rules?

Accepted Answer

Specificity

Answer

Support

Answer

Confidence

Answer

Lift

Question 21

Which of the following is not a measure of association in association rule mining?

Accepted Answer

Accuracy

Answer

Support

Answer

Lift

Answer

Confidence

Question 22

Which of the following is a benefit of using association rules in data mining?

Accepted Answer

Identifying patterns and trends in data

Answer

Clustering data points

Answer

Classifying data points

Answer

Predicting future outcomes

Question 23

In association rule mining, the lift of a rule measures the:

Accepted Answer

Degree to which the rule is more accurate than chance

Answer

Number of transactions that contain the rule

Answer

Confidence of the rule

Question 24

Which of the following is NOT a potential application of association rule mining?

Accepted Answer

Drug discovery

Answer

Fraud detection

Answer

Recommendation systems

Answer

Market basket analysis

Question 25

The Apriori algorithm aims to discover:

Accepted Answer

Frequent itemsets and association rules from a given dataset

Answer

Clusters in a dataset

Answer

Outliers in a dataset

Question 26

Which parameter is crucial for the Apriori algorithm's operation?

Accepted Answer

Minimum support threshold

Answer

Number of clusters

Answer

Learning rate

Question 27

In association rule mining, the support measure primarily determines:

Accepted Answer

How often an itemset occurs in the dataset

Answer

The predictive power of an association rule

Answer

The correlation between two items

Question 28

Market basket analysis is a typical application of:

Accepted Answer

Association rule mining

Answer

Clustering

Answer

Regression analysis

Answer

Natural language processing

Question 29

Distinguish between a frequent itemset and an association rule:

Accepted Answer

A frequent itemset is a set of co-occurring items, while an association rule describes a relationship between two frequent itemsets

Answer

There is no distinction between a frequent itemset and an association rule

Question 30

A key advantage of association rule mining is:

Accepted Answer

Unveiling hidden patterns and relationships within data

Answer

Improving the accuracy of predictive models

Answer

Reducing the dimensionality of the data

Question 31

What is the purpose of the minimum support threshold in the Apriori algorithm?

Accepted Answer

To determine which itemsets are considered frequent

Answer

To calculate the confidence of rules

Answer

To generate candidate itemsets

Question 32

What is the purpose of the candidate generation step in Apriori?

Accepted Answer

To generate potential association rules

Answer

To calculate the confidence of rules

Answer

To prune infrequent itemsets

Answer

To identify frequent itemsets

Question 33

What is the key difference between a frequent itemset and an association rule?

Accepted Answer

An association rule implies a relationship between multiple items, while a frequent itemset simply shows the co-occurrence of items

Answer

They are the same concept

Question 34

Which of the following is **NOT** an application of association rule mining?

Accepted Answer

Natural language processing

Answer

Market basket analysis

Answer

Fraud detection

Answer

Customer segmentation

Question 35

What is a major drawback of the Apriori algorithm?

Accepted Answer

It can be computationally expensive for large datasets

Answer

It is not scalable to high-dimensional data

Answer

It cannot handle missing values

Answer

It is only applicable to categorical data

Question 36

What is the difference between a strong association rule and a weak association rule?

Accepted Answer

A strong rule has high confidence and support, while a weak rule has low confidence and support

Answer

Strong rules can only be generated from frequent itemsets

Answer

They have the same strength

Question 37

Which of the following is a common evaluation metric for association rule mining algorithms?

Accepted Answer

F-measure

Answer

Recall

Answer

Precision

Answer

Accuracy

Question 38

Which of the following parameters directly affects the efficiency of the Apriori algorithm?

Accepted Answer

Minimum support and minimum confidence

Answer

Number of features and data distribution

Answer

Dataset size and number of transactions

Answer

Apriori confidence and lift

Question 39

The 'generate' step in the Apriori algorithm is used to:

Accepted Answer

Generate candidate itemsets of larger size from smaller ones

Answer

Prune infrequent candidate itemsets

Answer

Calculate the support and confidence of itemsets

Answer

Identify frequent itemsets

Question 40

The purpose of setting a minimum confidence threshold is to:

Accepted Answer

Filter out weak association rules that may not be meaningful

Answer

Increase the number of generated rules for more comprehensive analysis

Answer

Reduce the computational cost of the algorithm by eliminating unnecessary calculations

Answer

Improve the accuracy of the rules by ensuring they meet a certain level of statistical significance

Question 41

Which of the following is a measure that indicates the strength and relevance of an association rule?

Accepted Answer

Lift

Answer

Chi-squared

Answer

Confidence

Answer

Support

Question 42

When comparing a strong association rule to a valid association rule, the key difference is:

Accepted Answer

Strong rules imply a stronger relationship between items, while valid rules simply meet the minimum support and confidence thresholds

Answer

Strong rules are more likely to be statistically significant, while valid rules may not be

Answer

Strong rules are more useful for decision-making, while valid rules are more theoretical

Answer

There is no difference between a strong association rule and a valid association rule

Question 43

In the Apriori algorithm, the 'support' of an itemset is defined as:

Accepted Answer

The proportion of transactions that contain the itemset to the total number of transactions in a dataset

Answer

The number of unique items in the itemset

Answer

The total number of transactions in a dataset

Question 44

The 'confidence' of an association rule, which is measured as a percentage, represents:

Accepted Answer

The probability of finding the consequent given the antecedent

Answer

The ratio of the support of the rule to the support of the consequent

Answer

The probability of finding the antecedent given the consequent

Question 45

Which type of dataset is most commonly analyzed using association rule mining techniques?

Accepted Answer

Transaction data

Answer

Numerical data

Answer

Sequential data

Question 46

In the Apriori algorithm, candidate itemsets of size k are generated by:

Accepted Answer

Combining all itemsets of size (k-1) with high support

Answer

Removing infrequent itemsets from the dataset

Answer

Selecting itemsets based on their confidence

Question 47

Which of the following is a measure of the quality of an association rule that quantifies the deviation between the observed and expected frequencies?

Accepted Answer

Lift

Answer

Accuracy

Answer

Entropy

Answer

Specificity

Question 48

In an association rule, the 'antecedent' refers to:

Accepted Answer

The set of items that appear on the left-hand side of the rule

Answer

The set of items that are both frequent and highly correlated

Answer

The set of items that appear on the right-hand side of the rule

Question 49

Which of the following is a real-world application of association rule mining?

Accepted Answer

Market basket analysis

Answer

Image recognition

Answer

Natural language processing

Question 50

In an association rule, the 'consequent' refers to:

Accepted Answer

The set of items that appear on the right-hand side of the rule

Answer

The set of items that appear on the left-hand side of the rule

Answer

The set of items that are both frequent and highly correlated

Question 51

Which of the following statistical tests is commonly used to assess the statistical significance of an association rule?

Accepted Answer

Chi-square test

Answer

Binomial test

Answer

Pearson correlation coefficient

Answer

Jensen-Shannon divergence