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Data Mining and Redundant Rule Assignment

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Added on  2020-03-15

Data Mining and Redundant Rule Assignment

   Added on 2020-03-15

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Data Mining and Redundant Rule Assignment_1
Question 1(i)Rule #1: For a customer who initiates purchase of brushes, there exists a conditionalprobability of 1 that the same customer would also purchase nail polish.Rule #2: For a customer who initiates purchase of nail polish, there exists a conditionalprobability of 0.6322 that the same customer would also purchase brushes.Rule #3: For a customer who initiates purchase of nail polish, there exists a conditionalprobability of 0.5920 that the same customer would also purchase bronzer..1
Data Mining and Redundant Rule Assignment_2
(ii)Definition: If the given rule’s support level is predictable from the rule acting as theancestor, then the given rule would be termed as a redundant rule (Zaki, 2000).Application: Rule 2 can be taken as an apt exampleRule 1 (Support Level) = Rule 2 (Support Level) = 2.8735 Additionally, rule 1(Confidence level) > rule 2(Confidence level)Hence, confirmation of redundancy for rule#2Utility: It makes sense that the association rules are not considered in an isolated mannerbut rather collectively as this would result in maximization of the utility of these rules.Also, while collective use is imperative, the imperative characteristics particularly in termsof support and confidence remain important (Liebowitz, 2015)..(iii) XLMiner output (Increased minimum confidence interval to 75%)2
Data Mining and Redundant Rule Assignment_3

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