2) A database has four transactions. Let min sup = 60% and min.conf = 80%. %3D TID items_bought (in the form of brand-item.category) {King's-Crab, Sunset-Milk, Dairyland-Cheese, Best-Bread} {Best-Cheese, Dairyland-Milk, Goldenfarm-Apple, Tasty-Pie, Wonder-Bread} {Westcoast-Apple, Dairyland-Milk, Wonder-Bread, Tasty-Pie} {Wonder-Bread, Sunset-Milk, Dairyland-Cheese} cust ID 01 T100 02 T200 01 Т300 03 T400 (a) At the granularity of item_category (e.g., item; could be "Milk"), for the following rule template, VX € transaction, buys(X, item1) A buys(X, item2) = buys(X, item3) [s, c] list the frequent k-itemset for the largest k, and all of the strong association rules (with their support s and confidence c) containing the frequent k-itemset for the largest k. (b) At the granularity of brand-item.category (e.g., item; could be "Sunset-Milk"), for the following rule template, VX € customer, buys(X, item1) A buys(X, item2) = buys(X, item3) list the frequent k-itemset for the largest k (but do not print any rules).

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Fundamentals of Datawarehouse

A data warehouse is a data repository which is used to store large quantities of historical data that are mostly used for creating reports that help businesses to identify their weaknesses and strengths. For example, demographics data about people of a region can help businesses identify the needs and preferences of the region’s population. A data warehouse acts as a central storage for storing data that comes from one or more heterogenous sources. It also consists of data which can be old and of historical value. One of the most important applications of this type of system is that it is used for supporting business intelligence and data analytics applications.