Data Mining Chapter 11 Homework

Department of Computer Science
Database and Data Mining, COS 514
Dr. Chi Shen
Homework No. 8, Chapter 13, Aklilu Shiketa

Q13. 3 Cosmetic Purchases
Consider the following Data on Cosmetics Purchases in Binary Matrix Form

a) Select several values in the matrix and explain their meaning.
Value
Cell
Meaning
0
For example, Row 1, Column2
At transaction #1 bag was not purchased. (shows absence of Bag in the transaction)
1
Row 10, column (2 and 3)
“If a Bag is purchased, a Blush is also purchased at that same transaction.” (“If Bag, then Blush.”) While Bag is antecedent, Blush represents consequent.
1
Row 5, Column (3, 6, 8)
“If Blush and Concealer, then Bronzer. Item set {Blush, Concealer} = antecedent; { Bronzer} = consequent
1
Row 3, Column 1 If Blush and Concealer, then Eyebrow Pencils. While Eyebrow Pencil is associated with Blush and Concealer it is unassociated with the rest of the items.
12
Row 1 Column 1
Number of transactions.
13.3 b)
Consider the results of the association rules analysis shown in below.

I) For the first row, explain the “conf. %” output and how it is calculated.
It includes the following interpretations:
The Confidence of rule # 2 is 60.19 %( Or it is marginally over 60 %.) Confidence shows the rate at which consequents will occur.
In this case the consequents are Brushes and Concealer as the Rule goes “If Bronzer and Nail Polish, then Brushes and Concealer”
In this we are telling how any times Brushes and Concealers appear in transactions that contain Bronzer and Nail Polish.
It is calculated as follows.
Confidence = {transactions with antecedent and consequent items}/{transactions with antecedent items}
According to the values in the matrix:
While support of all transactions with Brushes, Concealer, Bronzer, Nail Polish are 62 (support a U c) and support of the number of transactions that involved antecedents (Bronzer and Nail Polish) are 103. = Confidence = {transactions with antecedent and consequent