14.4 Cosmetics Purchases. The data shown in Table 14.14 and the output in Table 14.15 are based on a subset of a dataset on cosmetic purchases (Cosmetics.csv) at a large chain TABLE 14.14 EXCERPT FROM DATA ON COSMETICS PURCHASES IN BINARY MATRIX FORM Trans. # Bag Blush Nail Polish Brushes Concealer Eyebrow Pencils Bronzer 1 0 1 1 1 1 0 1 2 0 0 1 0 1 0 1 3 0 1 0 0 1 1 1 4 0 0 1 1 1 0 1 5 0 1 0 0 1 0 1 6 0 0 0 0 1 0 0 7 0 1 1 1 1 0 1 8 0 0 1 1 0 0 1 9 0 0 0 0 1 0 0 10 1 1 1 1 0 0 0 11 0 0 1 0 0 0 1 12 0 0 1 1 1 0 1 TABLE 14.15 ASSOCIATION RULES FOR COSMETICS PURCHASES DATA lhs rhs support confidence lift 1 {Blush, Concealer, Mascara, Eye.shadow, Lipstick} => {Eyebrow.Pencils} 0.013 0.3023255814 7.198228128 2 {Trans., Blush, Concealer, Mascara, Eye.shadow, Lipstick} => {Eyebrow.Pencils} 0.013 0.3023255814 7.198228128 3 {Blush, Concealer, Mascara, Lipstick} => {Eyebrow.Pencils} 0.013 0.2888888889 6.878306878 4 {Trans., Blush, Concealer, Mascara, Lipstick} => {Eyebrow.Pencils} 0.013 0.2888888889 6.878306878 5 {Blush, Concealer, Eye.shadow, Lipstick} => {Eyebrow.Pencils} 0.013 0.2826086957 6.728778468 6 {Trans., Blush, Concealer, Eye.shadow, Lipstick} => {Eyebrow.Pencils} 0.013 0.2826086957 6.728778468

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 crosssales, and guidance for piloting an eventual time-of-purchase electronic recommender system to boost cross-sales. Consider first only the data shown in Table 14.14, given in binary matrix form. a. Select several values in the matrix and explain their meaning. b. Consider the results of the association rules analysis shown in Table 14.15. i. For the first row, explain the "confidence" output and how it is calculated. ii. For the first row, explain the "support" output and how it is calculated. iii. For the first row, explain the "lift" and how it is calculated. iv. For the first row, explain the rule that is represented there in words. c. Now, use the complete dataset on the cosmetics purchases (in the file Cosmetics.csv). Using R, apply association rules to these data (use the default parameters). i. Interpret the first three rules in the output in words. ii. Reviewing the first couple of dozen rules, comment on their redundancy and how you would assess their utility.