: Design algorithms for association rule mining algorithms

Which algorithm is commonly used for association rule mining?Support Vector MachinesDecision TreesK-MeansApriori

Association mining is used to discover:Patterns in structured data.Trends and anomalies in time-series data.Clusters of similar data points.Relationships between variables in a dataset.

What does support represent in association rule mining?The frequency of co-occurrence of items in transactions.The confidence of the association rule.The significance of the association rule.The size of the dataset.Clear selection

The Apriori algorithm is used for:RegressionClassificationClusteringAssociation Rule Mining

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)?