Data Mining and Visualization for Business Intelligence

Data Mining and Visualization for Business Intelligence
Assignment - 3
[Pick the date]
Student Name
Contents
1.1 Association Rules................................................................................................ 2
1.1.1 I).............................................................................................................. 2
1.1.2 II)............................................................................................................. 3
1.1.3 III)............................................................................................................ 4
1.2 Cluster Analysis............................................................................................. 4
1.2.1 A)............................................................................................................. 4

1.2.2 B)............................................................................................................. 4
1.2.3 C)............................................................................................................. 4
1.2.4 D)............................................................................................................. 5
1.2.5 e)............................................................................................................. 5
1.1 Association Rules
1.1.1 I)
Interpretation of the first 3 rules:
Rule1:
It means when the customer buys Brushes & Concealer known as antecedent, they
also buys Bronzer & Nail Polish. This is observed many times which gives the
incident 80% confidence. Support here for event A is 77 while event C has support in
103 instances. The number of times both the events happened were 62, giving the lift
ratio of 3.90. (Gupta, Garg, & Sharma, 2014; Rajak & Gupta, 2008; Sujatha & CH,
2011).
Rule2:
It is opposite to the rule 1 and should be interpreted as, when the customer buys Nail
Polish & Bronzer they also tends to buy Brushes & Concealer. Support for event A is
103 while for event C is 77 & the intersection of the event is 62. But the confidence is
for the rule is as low as 50%.
Rule3:
If any customer purchase nail polish, concealer & bronzer together then they also buy
brushes & it has confidence of 81%.

1.1.2 II)
To better understand the efficiency of the rules generated from the algorithm, there are various
criteria. Firstly we need to examine the level of confidence which gives shows the confidence for
the rules. Also, it should be logical & backed by the business understanding. For example, the
Rule 6 has Confidence level more than 80% & the lift ratio of 3.7. Also, this rules make logical
sense. Hence, this rule can be considered as efficient rule to apply.
1.1.3 III)
When the confidence level is set at 75% then the no. of association rules will reduce.
This is because the algorithm will only choose those rules in which confidence level
is more than or equal to 75%. Confidence Level is calculated by taking the proportion
of the support for A&C to support for A only. Hence, more transaction with the
intersection between antecedent & consequent is required to qualify as rules.