Data Mining Review Questions / XLMiner Labs Chapter 13 - Association Rules 1. Cosmetics Purchases. The data shown in Figure 13.7 (on page 278 of your textbook) are a subset of a dataset on cosmetic purchases given in binary matrix format. The complete dataset (in the file Cosmetics-small.xls) contains data on the purchases of different cosmetic items at a large chain drugstore. The store wants to analyze associations among purchases of these items for purposes of point-of-sale display, guidance to sales personnel in promoting cross sales, and guidance for piloting an eventual time-of-purchase electronic recommender system to boost cross sales (textbook reference - 13.3). a. Select several values in the matrix on page 333 (Figure 14.11) and explain their meaning. b. Consider the results of the association rules analysis shown in Figure 14.4 on page 334: i. For the first row, explain the \"Conf.%\" output and how it is calculated. ii. For the first row, explain the \"Support(a),\" \"Support(c),\" and \"Support(a U c)\" output and how it is calculated. iii. For the first row, explain the \"Lift Ratio\" and how it is calculated. iv. For the first row, explain the rule that is represented there (in your own words). c. Using XLMiner, apply association rules to the file Cosmetics-small.xls. Note: Do NOT include the Transaction # column in the XLMiner Data Range and accept the default Minimum Confidence (%) of 50. i. Interpret the first three rules in the output (in your own words). ii. Reviewing the first couple of dozen rules, comment on the rules' redundancy and how you would assess the rules' utility. iii. What would be the impact to the resulting rules if the Minimum Confidence (%) was raised to 75? Discuss why this occurs. Page 1 of 1 Data Mining Review Questions / XLMiner Labs Chapter 14 - Association Rules 1. Cosmetics Purchases. The data shown in Table 14.11 (on page 333 of your textbook) are a subset of a dataset on cosmetic purchases given in binary matrix format. The complete dataset (in the file Cosmetics-small.xls) contains data on the purchases of different cosmetic items at a large chain drugstore. The store wants to analyze associations among purchases of these items for purposes of point-of-sale display, guidance to sales personnel in promoting cross sales, and guidance for piloting an eventual time-of-purchase electronic recommender system to boost cross sales (textbook reference - 14.3). a. Select several values in the matrix on page 333 (Table 14.11) and explain their meaning. b. Consider the results of the association rules analysis shown in Figure 14.4 on page 334: i. For the first row, explain the \"Conf.%\" output and how it is calculated. ii. For the first row, explain the \"Support(a),\" \"Support(c),\" and \"Support(a U c)\" output and how it is calculated. iii. For the first row, explain the \"Lift Ratio\" and how it is calculated. iv. For the first row, explain the rule that is represented there (in your own words). c. Using XLMiner, apply association rules to the file Cosmetics-small.xls. Note: Do NOT include the Transaction # column in the XLMiner Data Range and accept the default Minimum Confidence (%) of 50. i. Interpret the first three rules in the output (in your own words). Page 1 of 3 ii. Reviewing the first couple of dozen rules, comment on the rules' redundancy and how you would assess the rules' utility. iii. What would be the impact to the resulting rules if the Minimum Confidence (%) was raised to 75? Discuss why this occurs. Page 2 of 3 Page 3 of 3