ECLAT Algorithm Overview Answers 1. What is a significant disadvantage of the ECLAT algorithm? A. It is unable to handle large databases effectively. B. It generates numerous candidate sets like the Apriori algorithm. C. It cannot be used for creating association rules. D. It requires substantial memory for storing pre-calculated equivalence classes. Answer: It requires substantial memory for storing pre-calculated equivalence classes. (D) ECLAT's substantial memory requirement is a known disadvantage, especially for large databases and frequent itemsets. 2. How does ECLAT compare to the Apriori algorithm? A. ECLAT is generally faster and more scalable than Apriori. B. ECLAT generates fewer candidate sets than Apriori. C. ECLAT and Apriori have equal efficiencies in processing large datasets. D. ECLAT is slower but more memory-efficient than Apriori. Answer: ECLAT is generally faster and more scalable than Apriori. (A) ECLAT is recognized for its speed and scalability in comparison to the Apriori algorithm. 3. What does ECLAT primarily focus on? A. Data visualization techniques. B. Database storage optimization. C. Frequent itemset mining. D. Association rule generation. Answer: Frequent itemset mining. (C) ECLAT is primarily designed for mining frequent itemsets. 4. What impact does the efficiency of the ECLAT algorithm have on association rules? A. It complicates the understanding of association rules. B. It enhances the speed and quality of generating association rules. C. It is irrelevant to the generation of association rules. D. It limits the types of association rules that can be generated. Answer: It enhances the speed and quality of generating association rules. (B) The efficiency of ECLAT positively influences the speed and quality of association rule generation. 5. What is a key characteristic of the complexity associated with ECLAT? A. It relies solely on simple data structures. B. It never causes issues with large datasets. C. Understanding traversal techniques is essential. D. It requires no prior knowledge of data mining. Answer: Understanding traversal techniques is essential. (C) The complexity of ECLAT necessitates a good understanding of data structures and traversal techniques. 6. What is the primary advantage of the ECLAT algorithm over Apriori-based algorithms? A. It generates all possible itemsets for testing. B. It is limited to small datasets. C. It uses equivalence classes to enhance performance. D. It relies on sequential itemset generation. Answer: It uses equivalence classes to enhance performance. (C) ECLAT's performance enhancement is primarily due to its use of equivalence classes. 7. Which phase of the ECLAT algorithm is focused on finding frequent itemsets? A. Data Collection B. Initialization C. Mining Frequent Itemsets D. Pruning Answer: Mining Frequent Itemsets (C) The core task of the ECLAT algorithm revolves around mining frequent itemsets. 8. In the context of ECLAT, what do equivalence classes represent? A. Groups of transactions that are similar. B. Items that appear together in transactions. C. Items sharing common properties in transactions. D. Completely unrelated item sets. Answer: Items sharing common properties in transactions. (C) Equivalence classes refer specifically to items that share common properties. 9. What is one key method ECLAT employs to improve its search efficiency? A. Using a sequential search algorithm. B. Pruning large numbers of candidate itemsets. C. Utilizing only static data structures. D. Generating all candidate itemsets. Answer: Pruning large numbers of candidate itemsets. (B) ECLAT improves efficiency by pruning unnecessary candidate itemsets early in the process. 10. How does the optimized hash tree variant of ECLAT contribute to its performance? A. It organizes data into a sequential format. B. It reduces the number of itemsets under consideration. C. It stores all transactions for easy access. D. It uses a complex algorithm with less efficiency. Answer: It reduces the number of itemsets under consideration. (B) The hash tree helps in reducing the itemsets considered, thus speeding up the process. 11. Which characteristic does not describe the ECLAT algorithm? A. Inability to handle large datasets. B. Effective for large transactions. C. High efficiency in mining itemsets. D. Employs a divide-and-conquer approach. Answer: Inability to handle large datasets. (A) ECLAT is well-suited for large datasets and does not have difficulty in this area. 12. What is the role of the pruning phase in the ECLAT algorithm? A. To collect data for processing. B. To ensure all candidate itemsets are tested. C. To eliminate redundant or infrequent sets. D. To combine equivalent itemsets. Answer: To eliminate redundant or infrequent sets. (C) Pruning helps in improving the algorithm's efficiency by removing unnecessary itemsets early. 13. What is a key feature of how ECLAT handles large transactions? A. It employs hash trees specialized for bulk data. B. It performs poorly with many items per transaction. C. It ignores transaction size during processing. D. It uses aggregation to simplify transactions. Answer: It employs hash trees specialized for bulk data. (A) ECLAT uses hash trees to effectively manage and process large transactions.