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).

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.

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). 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 A drug store chain wants to learn more about cosmetics buyers purchase patterns. Specifically, they want t what items are purchased in conjunction with each other, for purposes of display, point of sale special offers eventually implement a real time recommender system to cross-sell items at time of purchase. The data are in the form of a matrix in which each column represents a product group, and each row a custo transaction. Note: Data are from Peter Bruce, partially drawn from a real source unrelated to cosmetics and partially gen ns. Specifically, they want to know y, point of sale special offers, and to me of purchase. group, and each row a customer cosmetics and partially generated. 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 Transaction # 1a.: cosmetics Purchases. The date shown in IahleJiJi end the output 1:11 Enigma are based on a subset bfa dataset on eesrneiie purchases [Eusmeucs ..11.'_ c] at a large ehain magstsre. The store wants to analyze associations among parrohasas at these items at pupases oi polri-of-sale display. puldanoa to sales personnel In promoting cross sales. and guidance nor piloting an eventual time-of-purehase eleoo'orHo raoommandar system to boost arose sales consider rst only the data shown in intake 11 1 i. glyen ln binary matrix form. IableJL Excerpt from data on cosmetics purchases In binary matrix ton-n 1 o 1 1 | 1 1 o 1 I 2 o ' o 1 I o 1 o 1 I e o ' 1 o I o 1 1 1 I a 'T'p 1 I' 1 ' 1 o 1 'I s o ' 1 o I o 1 o 1 I a o ' o o I o 1 o o I 17 _o_'_1_ _1I'1_'1 o _1'__i e o o 1 I 1 . o o 1 I a o o o | o 1 1 o o 1 1o 1 1 1 |"1a" o _oI 11 o o 1 I o I o o 1 I 1: o o 1 I 1 1 o 1 I