Let us develop a new algorithm for the computation of all large itemsets. Assume that we are given a relation D similar to the Purchases table shown in Figure 26.1. We partition the table horizontally into k parts D1. Dk 1. Show that, if itemset X is frequent in D, then it is frequent in at least one of the k parts. 2. Use this observation to develop an algorithm that computes all frequent itemsets in two scans over D. (Hint: In the first scan, compute the locally frequent itemsets for each part Di, i E 1,..., k?.) 3. Ilustrate your algorithm using the Purchases table shown in Figure 26.1. The first partition consists of the two transactions with transid 111 and 112, the second partition consists of the two transactions with transid 113 and 114. Assume that the minimum support is 70 percent. Let us develop a new algorithm for the computation of all large itemsets. Assume that we are given a relation D similar to the Purchases table shown in Figure 26.1. We partition the table horizontally into k parts D1. Dk 1. Show that, if itemset X is frequent in D, then it is frequent in at least one of the k parts. 2. Use this observation to develop an algorithm that computes all frequent itemsets in two scans over D. (Hint: In the first scan, compute the locally frequent itemsets for each part Di, i E 1,..., k?.) 3. Ilustrate your algorithm using the Purchases table shown in Figure 26.1. The first partition consists of the two transactions with transid 111 and 112, the second partition consists of the two transactions with transid 113 and 114. Assume that the minimum support is 70 percent