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Data Mining- Assignment

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Added on  2020-03-16

Data Mining- Assignment

   Added on 2020-03-16

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Data Mining[Pick the date]Student Id and Name
Data Mining- Assignment_1
1. Association rule For the issued data set the association rule has been applied through XLMiner Analyticalplatform. The generated output from XLMiner is furnished below:It is apparent from the inputs that minimum confidence is considered as 50%. (i)Comment on the primary three rules as per the list of rules table resulted throughXLMiner is shown below (Ana, 2014).Rule 1 It is apparent from the first row of the list of rules table that there is 100% confidence that aperson who is going to buy brush item will also buy nail polish item. Rule 2 It is apparent from the second row of the list of rules table that there is 63.22 % confidence that aperson who is going to buy nail polish item will also buy brush item. 1
Data Mining- Assignment_2
Rule 3 It is apparent from the third row of the list of rules table that there is 59.20 % confidencepercentage that a person who is going to buy nail polish item will also buy bronzer item. In this context, it is essential to note that there are two key characteristics of any association rulenamely the lift ratio and also confidence level. The importance or support of an underlying ruleis characterized by the lift ratio which ideally should be higher as association rules are arrangedin decreasing order. Also, the confidence level is indicative of the conditional probability of theconsequent purchase happening assuming antecedent purchase has happened.(i)In order to analyze the first 24 rules, the support level is decreased to 50 which leads to thefollowing rules.Redundancy of association rules is often a common problem and hence rectifications needto be made in the form of deleting such rules whose incremental value is lacking as they2
Data Mining- Assignment_3
tend to communicate the same information as has been communicated through a separaterule. In case of given cosmetic data and the associated output indicated above, there are anumber of redundant rules that are identified below (Ragsdale, 2014).Rule 2 (with respect to Rule 1 as same lift ratio and predictable output)Rule 4(with respect to Rule 3 as same lift ratio and predictable output)Rule 6(with respect to Rule 5 as same lift ratio and predictable output)Rule 8(with respect to Rule 7 as same lift ratio and predictable output)Rule 10(with respect to Rule 9 as same lift ratio and predictable output)Rule 12(with respect to Rule 11 as same lift ratio and predictable output)Rule 14(with respect to Rule 13 as same lift ratio and predictable output)Rule 16(with respect to Rule 15 as same lift ratio and predictable output)It needs to be understood that association rules are derived so that key insights into the consumerbuying behavior needs to be understood. Hence, any redundant rules need to be deleted. Further,In the interpretation of the remaining rules, two critical parameters essentially relate to the liftratio and the confidence level which essentially determine the significance and support to theunderlying rule. In this manner, vital information may be communicated with respect to theexpected buying behavior of the customer which then can be used for decision making. Forinstance, items such as brush and nail polish may be placed on closed proximity so as to facilitatecustomer buying. Also, specific consumer traits that are most profitable to a given companyrelated to purchase of cosmetics can be encouraged (Shumulei et. al., 2016).(ii) Now, the confidence percentage has been changed and become 75% from 50%. Thechange in the result can be viewed as shown below:3
Data Mining- Assignment_4

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