Question

A database has five transactions. Let min sup = 60% and min conf = 80%.

TID items

T100 = {M, O, N, K, E, Y}

T200 = {D, O, N, K, E, Y}

T300 = {M, A, K, E}

T400 = {M, U, C, K, Y}

T500 = {C, O, O, K, I, E}

Find all frequent itemsets of a single item using the Apriori algorithm.

Answer 1

The Apriori algorithm is used to find frequent itemsets in a database based on a given support threshold. In this case, the frequent itemsets of a single item would be {M}, {O}, {N}, {K}, {E}, and {Y}, as they all have a support of 100%.

Step-by-step explanation:

The Apriori algorithm is a popular algorithm used in data mining and association rule learning. It is used to find frequent itemsets in a database based on a given support threshold. The algorithm works by generating candidate itemsets of increasing size and then pruning them if their support is below the minimum support threshold.

In this case, the database has five transactions and the minimum support is set to 60%. To find all frequent itemsets of a single item, we start by counting the occurrences of each individual item (counting how many times they appear in the transactions) and then comparing their support to the minimum support threshold.

The frequent itemsets with a support higher than 60% will be considered frequent. In this case, the frequent itemsets of a single item would be {M}, {O}, {N}, {K}, {E}, and {Y}, as they all have a support of 100% (appearing in all transactions).