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(c) Insert the necessary transactions such that at least two new
frequent itemsets are discovered, keeping the same support.
9.5
Given the transaction database below, manually run the FP-growth
algorithm with minimum support of 3.
TID
Items
{}
{
T1
A,B
}
T2
B,C,D
{
}
T3
A,C,D,E
T4
{
A,D,E
}
{
}
T5
A,B,C
{
}
T6
A,B,C,D
{}
T7
A
{
}
T8
A,B,C
T9
{
A,B,D
}
{
}
T10
B,C,E
9.6
Consider again the Foodmart data warehouse:
(a) Use the
Sales
table to build a transaction database for association
rule mining. Use this table to produce an association rule model.
(b) Explore the model, and write a query to find out the 10 most
frequent itemsets.
(c) Write a DMX query that, given the purchase of a product by a
customer, selects other products to recommend her.
9.7
Use the
Sales
,
Product
,and
Product class
tables to build a table contain-
ing transactions for hierarchical association rule mining. The hierarchy
in the
Product class
table contains the levels
product subcategory
→
product family
.Wewantto
know, given a purchase of a product of a class
C
, which is the class
of a product that will be bought together with the former one, where
class is one of subcategory, category, department, or family. Using the
Product class
table, produce an association rule model and analyze it.
9.8
Implement in Analysis Services the KPIs defined in Sect.
9.2.3
for the
Northwind data warehouse.
9.9
The Foodmart company wants to define a collection of KPIs based
on its data warehouse. The finance department wants to monitor the
overall performance of the company stores, to check the percentage of
the stores accountable for 85% of the total sales (the Pareto's principle).
The sales department wants to monitor the evolution of the sales cost.
It also wants to measure the monthly rate of new customers.
Propose KPIs that can help the departments in these tasks. Define
these KPIs together with the goals that they are aimed to evaluate.
9.10
Define in Analysis Services the KPIs of Ex.
9.9
.
product category
→
product department
→
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