The two primary drawbacks of the Apriori Algorithm are: At each step, candidate sets have to be built.To build the candidate sets, the algorithm has to repeatedly scan the database.

The Apriori algorithm uses a generate-and-count strategy for deriving frequent itemsets.Candidate itemsets of size k + 1 are created by joining a pair of frequent itemsets of size k (this isknown as the candidate generation step).A candidate is discarded if any one of its subsets is found to be infrequent during the candidatepruning step. Suppose the Apriori algorithm is applied to the data set shown in the below Tablewith minsup = 30%, i.e., any itemset occurring in less than 3 transactions are considered to beinfrequent.(a) Draw an itemset lattice representing the data set.(b) What is the percentage of frequent itemsets.(c) What is the pruning ratio of the Apriori algorithm on this data set? (Pruning ratio is defined asthe percentage of itemsets not considered to be a candidate because (1) they are not generatedduring candidate generation or (2) they are pruned during the candidate pruning step.)(d) What is the false alarm rate (i.e, percentage of candidate itemsets that are found to be infrequentafter performing support counting)?

Which of the following is TRUE about apriori algorithm?I. Apriori algorithm uses the downward closure property.II. Apriori algorithm utilizes ‘pruning’ to control the exponential growth of candidate itemsets.

In which algorithm, we make sure that the frequent items appear early in each transaction?Select one:a. Apriori algorithmb. FP Growth

: Design algorithms for association rule mining algorithms

The Apriori algorithm is used for:RegressionClassificationClusteringAssociation Rule Mining