The List of Rules Generated by XLMiner - Desklib

Data
Mining
Student Id and Name
[Pick the date]

Question 1
XLMiner is used applying association rule on data variables. The inputs are as highlighted
below:
Number of transaction = 500
Number of variables = 7
Minimum confidence = 0.5 or 50%
XLMiner has generated the input and the part of lists of rules is as shown below:
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(i) After analyzing the list of rules generated by XLMiner, the following conclusion has been
derived regarding the first three association rules (Ana, 2014).
Rule 1 – As per first association rule, it would be fair to conclude that the concerned person who
first purchases brush would then purchase nail polish with an estimated confidence of 100%.
Rule 2 - As per second association rule, it would be fair to conclude that the concerned person
who first purchases nail polish would then purchase brush with an estimated confidence of
63.22%.
Rule 3 - As per third association rule, it would be fair to conclude that the concerned person who
first purchases nail polish would then purchase bronzer with an estimated confidence of 59.20%.
(ii) Redundancy of rule is a potential issue in the association rules and hence such rules need to
be trimmed. A particular case of redundancy for the given data pertains to rule 2 which has
exactly the same support or lift ratio as witnessed for rule 1. The only difference is that the
confidence level for rule 2 is lower than the corresponding level for rule 1 which makes
rule 2 inferior than rule 1 (Zaki, 2000).
Additionally, the utility of the association rule lies in the fact that they can enable identification
of hidden associations prevalent in consumer buying behavior. However, in order to use the same
a balance between support and confidence is required. This is because if the support is high, then
the rules regarding rare items rule are not displayed. However, if the support is kept at a low
value, hence the rules generated are quite more which tends to undermine the end utility of these
and hence not recommended (Liebowitz, 2015).
(iii) In this case, the input is same only the minimum confidence has changed and become
0.75 in place of 0.50.
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Minimum confidence = 0.75 or 75%
XLMiner has generated the input and the part of lists of rules is as shown below.
It would be fair to conclude that increase in the confidence percentage from 0.5 to 0.75, the
number of list of association rules displayed is decreased. This is because only the association
rules which has fall in the range above the selected minimum confidence percentage would
appear. Hence, the rules which display lower confidence percentage as compared with minimum
confidence percentage would be removed automatically through XLMiner. This could be
problematic since the rules not displayed may have high support levels and hence significant
(Ragsdale, 2014).
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