1. Suppose that a large store has a transaction database that is distributed among four locations. Transactions in each component database have the same format, namely T_j: {i₁; ; i_m}, where T_j is a transaction identifier and i_k (1<=k <= m) is the identifier of an item purchased in the transaction. Propose an efficient algorithm to mine global association rules.

You may present your algorithm in the form of an outline. Your algorithm should not require shipping all of the data to one site and should not cause excessive network communication overhead.

1. [Contributed by Tao Cheng] Most frequent pattern mining algorithms consider only distinct items in a transaction. However, multiple occurrences of an item in the same shopping basket, such as four cakes and three jugs of milk, can be important in transaction data analysis. How can one mine frequent itemsets efficiently considering multiple occurrences of items? Propose modifications to the well-known algorithms, such as Apriori and FP-growth, to adapt to such a situation.