Given a data set with five transactions, each containing five items, as shown in the table.

TID	items_bought
T1	{A, H, K, T, X}
T2	{A, H, X, T, Z}
T3	{A, B, D, R, S}
T4	{B, H, S, T, X}
T5	{B, H, G, M, S}

Let n be the total number of transactions, b be the number of items in each transaction, m be the number of k-itemset candidates. Consider the following two different approaches for counting the support values of the candidates. For each transaction, the first approach checks if a candidate occurred in the transaction or not; the second approach enumerates all the possible k-itemsets of the transaction and checks if the itemset is one of the candidates. What is the computation complexity for each approach?

Please post work steps for how you found the complexity. Thanks =)