What statements are correct? Support and confidence are the only method for the discovery of A itemset with a relative support that is larger than the minimum support threshold is called frequent. Association rules that satisfy both a minimum support threshold and a minimum confidence threshold are called strong. Support and confidence can be used for the discovery of correlation relationships. What statements are correct on the Aprior algorithm? Apriori is a seminal algorithm proposed by R. Agrawal and R. Srikant in 1994 for mining frequent itemsets for Boolean association rules. Apriori employs an iterative approach known as a level-wise search, where k-itemsets are used to explore (k+ 1)-itemsets Aprior algorithm uses Apriori property that all nonempty subsets of a frequent itemset must also be frequent. Apriori is a basic algorithm for finding frequent itemsets Which statements are correct about evaluating the discovered association rules? The lift measure (also called interest factor) has a number of statistical advantages over the confidence measure. These object measures (for example, lift, cosine, and correlation) on discovered association rules always give consistent results about the quality of discovered patterns. Thus, we can randomly choose a measure. The classical association rule mining formulation relies on the support and confidence measures to eliminate uninteresting patterns. But only the support and confidence measures are not enough. These object measures (for example, lift, cosine, and correlation) on discovered association rules may not always give consistent results and they may provide conflicting information about the quality of discovered patterns. Thus, we need to carefully choose some measures that are suitable for the applications that we work on