[amp_mcq option1=”partitioning” option2=”candidate generation” option3=”itemset eliminations” option4=”pruning” correct=”option4″]<\/p>\n
The correct answer is D. pruning<\/strong>.<\/p>\n Pruning is a technique used in association rule mining to eliminate candidate itemsets that are not likely to be frequent. This is done by checking the support of each candidate itemset against the minimum support threshold. If the support of a candidate itemset is below the minimum support threshold, it is pruned from the search space.<\/p>\n Partitioning is a technique used to divide the data set into smaller subsets. This is done by grouping together records that are similar. Partitioning can be used to improve the efficiency of association rule mining by reducing the number of candidate itemsets that need to be checked.<\/p>\n Candidate generation is a technique used to generate all possible candidate itemsets from a set of frequent itemsets. This is done by taking all possible combinations of frequent itemsets. Candidate generation can be a computationally expensive task, especially for large data sets.<\/p>\n Itemset elimination is a technique used to eliminate candidate itemsets that are not likely to be frequent. This is done by checking the support of each candidate itemset against the minimum support threshold. If the support of a candidate itemset is below the minimum support threshold, it is eliminated from the search space.<\/p>\n","protected":false},"excerpt":{"rendered":" [amp_mcq option1=”partitioning” option2=”candidate generation” option3=”itemset eliminations” option4=”pruning” correct=”option4″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[729],"tags":[],"class_list":["post-45642","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n