Introduction to Association Rule Learning Answers 1. What is the primary goal of pruning candidate itemsets in association rule mining? A. To increase the size of the candidate itemsets. B. To eliminate candidate itemsets that do not meet the minimum support threshold. C. To ensure all subsets of candidate itemsets are considered. D. To create larger itemsets from smaller ones. Answer: To eliminate candidate itemsets that do not meet the minimum support threshold. (B) Pruning is crucial for reducing the complexity of the mining process by eliminating itemsets that are not frequent. 2. Which of the following metrics is NOT commonly used in the evaluation of association rules? A. Support B. Confidence C. Lift D. Variance Answer: Variance (D) Variance is not a metric typically used in the evaluation of association rules; the focus is on support, confidence, and lift. 3. Which application of association rule mining involves analyzing user browsing patterns? A. Market Basket Analysis B. Web Usage Mining C. Recommendation Systems D. Customer Segmentation Answer: Web Usage Mining (B) Web Usage Mining specifically focuses on understanding user behavior and navigation patterns on websites. 4. What is a significant limitation of association rule mining related to data? A. High dimensionality of data leading to computational expense. B. Limited application scenarios. C. It can only handle binary data. D. Requires extensive manual interpretations of rules. Answer: High dimensionality of data leading to computational expense. (A) High dimensionality leads to increased computational cost, making association rule mining difficult to implement on large datasets. 5. What typically happens when minimum support and minimum confidence thresholds are set too low? A. Too few rules will be discovered. B. It becomes easier to interpret the rules. C. An excessive number of rules may be generated, including meaningless patterns. D. Only the most significant rules will be generated. Answer: An excessive number of rules may be generated, including meaningless patterns. (C) Lower thresholds can lead to discovering many rules, but not all will be meaningful or useful in practice. 6. Which algorithm is often preferred over Apriori for large datasets due to its efficiency? A. FP-Growth Algorithm B. Support Vector Machine C. Decision Tree D. K-means Clustering Answer: FP-Growth Algorithm (A) The FP-Growth Algorithm is known for requiring less memory and being faster compared to the Apriori algorithm. 7. In the context of association rule mining, what is a common application of identifying products frequently purchased together? A. Recommendation Systems B. Medical Diagnosis C. Fraud Detection D. Customer Segmentation Answer: Recommendation Systems (A) Recommendation Systems use frequent purchase patterns to suggest related products to consumers. 8. What is a challenge associated with the interpretability of discovered rules in association rule mining? A. Rules may be overly complex and difficult to interpret. B. The rules are always easy to understand. C. Interpretation requires no domain knowledge. D. Rules do not change frequently. Answer: Rules may be overly complex and difficult to interpret. (A) Complex rules can hinder practical application and understanding, making interpretation challenging for users. 9. What is the main purpose of association rule learning? A. To find correlations without a rule-based approach B. To discover interesting relationships between variables in large datasets C. To create predictive models for supervised learning D. To classify data into predefined categories Answer: To discover interesting relationships between variables in large datasets (B) Association rule learning focuses on identifying relationships and patterns in data through rules. 10. Which of the following statements accurately defines 'support' in association rule learning? A. The proportion of transactions that contain a specific itemset B. The measure of the potential future sales prediction C. The likelihood of an item appearing in a specific transaction D. The strength of the association rule as a probability Answer: The proportion of transactions that contain a specific itemset (A) Support quantifies how frequently items appear together in the dataset. 11. What does a lift value greater than 1 signify in association rule learning? A. The rule is considered weak and unreliable B. The items are likely to occur independently C. The items are more likely to occur together than by random chance D. The supporting transactions number is not significant Answer: The items are more likely to occur together than by random chance (C) A lift greater than 1 indicates a stronger relationship between the items than random occurrence. 12. Which step is NOT part of the Apriori algorithm? A. Generate clustering patterns to predict future transactions B. Perform multiple iterations to find all itemsets C. Generate candidate itemsets from previous frequent itemsets D. Scan the database to count the support of each item Answer: Generate clustering patterns to predict future transactions (A) The Apriori algorithm focuses on frequent itemsets, while clustering is a different technique. 13. In the context of association rules, what is the antecedent? A. The total number of unique items in the dataset B. The initial item that suggests another will likely be present C. The resulting item that follows the initial item D. The combination of items that have a high support value Answer: The initial item that suggests another will likely be present (B) The antecedent is the 'if' part of an association rule, suggesting the presence of the consequent. 14. Which of the following is an example of association rule? A. If a customer browses sports gear, then they buy either electronics or clothing B. If sales increase, then marketing expenditure has also increased C. If a customer buys bread, then they are likely to also buy milk D. If a customer visits a webpage, then they buy a product Answer: If a customer buys bread, then they are likely to also buy milk (C) The example provided represents a classic 'if-then' relationship characteristic of association rules. 15. How does the Apriori principle assist in finding frequent itemsets? A. By assuming all items are equally likely to co-occur B. By stating that frequent itemsets contain fewer items than rare itemsets C. By eliminating transactions that do not support a single item D. By indicating that frequent itemsets must have all subsets also be frequent Answer: By indicating that frequent itemsets must have all subsets also be frequent (D) The Apriori principle ensures that if an itemset is frequent, all of its subsets must also be frequent. 16. What is the significance of confidence in an association rule? A. It indicates availability of products in inventory B. It measures the reliability of the rule based on its frequency C. It represents the total number of transactions in the dataset D. It quantifies the diversity of items in a transaction Answer: It measures the reliability of the rule based on its frequency (B) Confidence measures the likelihood of the consequent occurring when the antecedent is present.