The Apriori algorithm uses a generate-and-count strategy for deriving frequent items ets. Candidate item sets of size k + 1 are created by joining a pair of frequent items ets of size k (this is known as the candidate generation step). A candidate is discarded if any one of its subsets is found to be infrequent during the candidate pruning step. Suppose the Apriori algorithm is applied to the data set shown in Table below with mins up = 30%, i.e., any item set occurring in less than 3 transactions is considered to be infrequent.

Solve problem by applying FP Algorithm..!!

Transaction ID Items Bought 1 {a,b,d,e} 2 {b,c,d) 3 {a,b,d,e} {a,c,d,e} 5 {b,c,d,e) 6 {b, d, e} 7 {c,d} 8 {a,b,c} 9 {a,d,e) 10 {b,d}