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Assignment of Data Mining Techniques

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Added on  2020-03-16

Assignment of Data Mining Techniques

   Added on 2020-03-16

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Assignment of Data Mining Techniques_1
Question 1The association rules have been derived based on the given data using XL Miner as an enablingtechnique and the following output has been obtained.(i)The interpretation in regards to the first three rules is offered below (Rouse, 2004).Rule 1 – With an underlying conditional probability of 1, it can be claimed that a customer whomakes the purchase of brushes would also tend to buy nail polish. Rule 2 - With an underlying conditional probability of 0.6322, it can be claimed that a customerwho makes the purchase of nail polish would also tend to buy brushes.1
Assignment of Data Mining Techniques_2
Rule 3 - With an underlying conditional probability of 0.5920, it can be claimed that a customerwho makes the purchase of nail polish would also tend to buy bronzer.(ii)A key shortcoming of association rules is in the form of rule redundancy. It is imperative toidentify such rules on the basis of ancestor or preceding rule. This can be explained withthe help of Rule 2 which is redundant here when viewed with Rule 1. An interestingobservation is that the underlying support level precisely matches for both the rules butonly difference in the confidence interval. Hence the redundancy of rule 2 is confirmed(Zaki, 2000).The association rules tend to complement each other and in totality tend to highlight acomplete picture of the underlying buying pattern or customer behavior. Further, theindividual utility of these rules must be determined keeping in mind the underlyingimportance (hinted by lift ratio) and the conditional probability (hinted by confidencelevel). Usually as a thumb rule, rules that tend to have high values in this regard can proveuseful and hence must be paid special attention (Shumueli et. al., 2016).(iii)The output of XL Miner with the requisite minimum confidence of 75% is underlinedbelow.2
Assignment of Data Mining Techniques_3

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