[amp_mcq option1=”apriori” option2=”brute force” option3=”dbscan” option4=”k-nearest neighbor” correct=”option1″]<\/p>\n
The correct answer is A. apriori.<\/p>\n
Apriori is an algorithm for frequent itemset mining, a task in data mining that involves finding all subsets of items that occur together in a transaction database with a frequency greater than or equal to a user-specified minimum support threshold.<\/p>\n
Brute force is a method of solving a problem by trying every possible solution. It is a very inefficient method, but it is guaranteed to find the correct solution if one exists.<\/p>\n
C. dbscan is a density-based clustering algorithm. It clusters data points based on their density, i.e., how many other data points are close to them.<\/p>\n
D. k-nearest neighbor is a lazy learning algorithm for classification and regression. It classifies a new data point by finding its k nearest neighbors in the training data and then assigning the class that is most common among the neighbors.<\/p>\n
Apriori is a more efficient algorithm than brute force, and it is also more likely to find all of the frequent itemsets in a database. Dbscan and k-nearest neighbor are not classification algorithms, so they are not relevant to the question.<\/p>\n","protected":false},"excerpt":{"rendered":"
[amp_mcq option1=”apriori” option2=”brute force” option3=”dbscan” option4=”k-nearest neighbor” correct=”option1″]<\/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-45147","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n