b. i-v please

Cosmetics Purchases. The data shown in Table 14.11 and the output in Figure 14.4 are based on a subset of a dataset on cosmetic purchases (Cosmetics.xls) 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. Consider first only the data shown in Table 14.11. given in binary Use several of the resulting rules. matrix form. Select several values in the matrix and explain their meaning. b. Consider the results of the association rules analysis shown in Figure 14.4. i. For the first row, explain the "Conf.%" output and how it is calculated. i. For the first row, explain the "Support for A," "Support for C," and "Support for A & 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 words. Now use the complete dataset on the cosmetics purchases (in the file Cosmet- ics.xls). v. Using XL Miner, apply association rules to these data (use the default parameters). vi. Interpret the first three rules in the output in words. vii. Reviewing the first couple of dozen rules, comment on their redundancy and how you would assess their utility. TABLE 14.11 EXCERPT FROM DATA ON COSMETICS PURCHASES IN BINARY MATRIX FORM Trans. Bag Blush Nail Polish Brushes Concealer Eyebrow Pencils Bronzer 1 2 3 1 1 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 1 1 1 0 1 0 0 1 1 0 1 1 1 1 0 0 1 0 0 1 1 OOOOOOOOOOO 1 1 1 1 1 0 1 1 0 10 11 O OOO 1 0 1 1 1 0 12 1 0 TABLE 14.4 FIFTY TRANSACTIONS OF RANDOMLY ASSIGNED ITEMS Transaction Items Transaction Items Transaction Items 00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 8 3 4 8 8 39 9 1 8 6 9 3 5 7 9 8 18 8 19 20 9 21 2 5 6 8 22 4 6 9 23 4 9 24 8 9 25 6 8 26 6 8 27 5 8 28 4 8 9 29 9 30 8 31 1 5 8 32 3 6 9 33 7 9 34 7 8 9 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 1 5 3 4 6 8 1 4 8 4. 7 8 8 9 4 5 7 9 2 8 9 2 5 9 1 2 7 9 5 8 1 7 8 8 2 7 9 4 6 9 9 9 6 7 8 LO 1 7 9 1 4 5 8 9 5 7 9 6 7 8 3 7 9 1 4 9 6 7 8 Cosmetics Purchases. The data shown in Table 14.11 and the output in Figure 14.4 are based on a subset of a dataset on cosmetic purchases (Cosmetics.xls) 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. Consider first only the data shown in Table 14.11. given in binary Use several of the resulting rules. matrix form. Select several values in the matrix and explain their meaning. b. Consider the results of the association rules analysis shown in Figure 14.4. i. For the first row, explain the "Conf.%" output and how it is calculated. i. For the first row, explain the "Support for A," "Support for C," and "Support for A & 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 words. Now use the complete dataset on the cosmetics purchases (in the file Cosmet- ics.xls). v. Using XL Miner, apply association rules to these data (use the default parameters). vi. Interpret the first three rules in the output in words. vii. Reviewing the first couple of dozen rules, comment on their redundancy and how you would assess their utility. TABLE 14.11 EXCERPT FROM DATA ON COSMETICS PURCHASES IN BINARY MATRIX FORM Trans. Bag Blush Nail Polish Brushes Concealer Eyebrow Pencils Bronzer 1 2 3 1 1 0 0 0 0 0 0 0 0 0 1 1 0 1 0 1 0 1 1 1 0 1 0 0 1 1 0 1 1 1 1 0 0 1 0 0 1 1 OOOOOOOOOOO 1 1 1 1 1 0 1 1 0 10 11 O OOO 1 0 1 1 1 0 12 1 0 TABLE 14.4 FIFTY TRANSACTIONS OF RANDOMLY ASSIGNED ITEMS Transaction Items Transaction Items Transaction Items 00 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 8 3 4 8 8 39 9 1 8 6 9 3 5 7 9 8 18 8 19 20 9 21 2 5 6 8 22 4 6 9 23 4 9 24 8 9 25 6 8 26 6 8 27 5 8 28 4 8 9 29 9 30 8 31 1 5 8 32 3 6 9 33 7 9 34 7 8 9 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 1 5 3 4 6 8 1 4 8 4. 7 8 8 9 4 5 7 9 2 8 9 2 5 9 1 2 7 9 5 8 1 7 8 8 2 7 9 4 6 9 9 9 6 7 8 LO 1 7 9 1 4 5 8 9 5 7 9 6 7 8 3 7 9 1 4 9 6 7 8