1. What is the primary objective of Association Rule Mining?
- A) To cluster data into groups
- B) To find hidden patterns or relationships between variables in a dataset
- C) To classify data into predefined categories
- D) To reduce the dimensionality of data
Answer: B) To find hidden patterns or relationships between variables in a dataset
Explanation: Association Rule Mining identifies frequent patterns, correlations, or associations between variables in large datasets.
2. Which of the following metrics is used to measure the frequency of an itemset in Association Rule Mining?
- A) Confidence
- B) Support
- C) Lift
- D) Leverage
Answer: B) Support
Explanation: Support measures the proportion of transactions in the dataset that contain a particular itemset.
3. What does the confidence metric represent in Association Rule Mining?
- A) The proportion of transactions that contain the antecedent
- B) The likelihood that the consequent is present given that the antecedent is present
- C) The strength of the association rule compared to random chance
- D) The frequency of the entire dataset
Answer: B) The likelihood that the consequent is present given that the antecedent is present
Explanation: Confidence is calculated as Confidence(A→B)=Support(A∪B)Support(A)\text{Confidence}(A \rightarrow B) = \frac{\text{Support}(A \cup B)}{\text{Support}(A)}.
4. Which of the following metrics is used to measure the strength of an association compared to random chance?
- A) Confidence
- B) Support
- C) Lift
- D) Conviction
Answer: C) Lift
Explanation: Lift evaluates the strength of an association rule compared to what would be expected if the items were independent.
5. In the context of Association Rule Mining, what does an Apriori algorithm do?
- A) Finds the optimal number of clusters
- B) Generates association rules by finding frequent itemsets
- C) Classifies data into predefined categories
- D) Reduces the dimensionality of data
Answer: B) Generates association rules by finding frequent itemsets
Explanation: The Apriori algorithm identifies frequent itemsets and uses them to generate association rules.
6. What is the main assumption behind the Apriori algorithm?
- A) If an itemset is frequent, all its subsets are also frequent.
- B) If an itemset is frequent, all its supersets are also frequent.
- C) Frequent itemsets are independent of their subsets.
- D) Frequent itemsets cannot be used to generate rules.
Answer: A) If an itemset is frequent, all its subsets are also frequent.
Explanation: This is known as the Apriori property and is used to reduce the search space in the algorithm.
7. Which of the following algorithms is an improvement over Apriori for finding frequent itemsets?
- A) K-means
- B) FP-Growth (Frequent Pattern Growth)
- C) CART
- D) Decision Tree
Answer: B) FP-Growth (Frequent Pattern Growth)
Explanation: FP-Growth is faster than Apriori as it avoids generating candidate itemsets and uses a compact data structure called the FP-tree.
8. What is a frequent itemset in Association Rule Mining?
- A) An itemset with a high lift value
- B) An itemset that appears in a large fraction of transactions
- C) An itemset with a high confidence value
- D) An itemset with more than two items
Answer: B) An itemset that appears in a large fraction of transactions
Explanation: A frequent itemset is defined as an itemset that meets a specified minimum support threshold.
9. Which of the following is NOT an application of Association Rule Mining?
- A) Market Basket Analysis
- B) Fraud Detection
- C) Predicting the next item in a sequence
- D) Customer Segmentation
Answer: C) Predicting the next item in a sequence
Explanation: Predicting the next item in a sequence falls under sequence analysis, not association rule mining.
10. What does a Lift value greater than 1 indicate?
- A) The items are negatively associated.
- B) The items are independent.
- C) The items are positively associated.
- D) The items are rare in the dataset.
Answer: C) The items are positively associated.
Explanation: A Lift value greater than 1 indicates that the presence of one item increases the likelihood of the other item occurring, suggesting a positive association.