Age Female Income Married Children Loan Mortgage 48 1 17546.00 0 1 0 0
40 0 30085.10 1 3 1 1
51 1 16575.40 1 0 1 0
23 1 20375.40 1 3 0 0
57 1 50576.30 1 0 0 0
57 1 37869.60 1 2 0 0
22 0 8877.07 0 0 0 0
58 0 24946.60 1 0 1 0
37 1 25304.30 1 2 1 0
54 0 24212.10 1 2 1 0
66 1 59803.90 1 0 0 0
52 1 26658.80 0 0 1 1
44 1 15735.80 1 1 0 1
66 1 55204.70 1 1 1 1
36 0 19474.60 1 0 0 1
38 1 22342.10 1 0 1 1
37 1 17729.80 1 2 0 1
46 1 41016.00 1 0 0 1
62 1 26909.20 1 0 0 0
31 0 22522.80 1 0 1 0
61 0 57880.70 1 2 0 0
50 0 16497.30 1 2 0 0
54 0 38446.60 1 0 0 0
27 1 15538.80 0 0 1 1
22 0 12640.30 0 2 1 0
56 0 41034.00 1 0 1 1
45 0 20809.70 1 0 0 1
39 1 20114.00 1 1 0 0
39 1 29359.10 0 3 1 1
61 0 24270.10 1 1 0 0
(a) Use hierarchical clustering with the matching coefficient as the similarity measure and the group average linkage as the clustering method to create nested clusters based on the Female, Married, Loan, and Mortgage variables. Specify the construction of 3 clusters. How would you characterize each cluster? Use a PivotTable on the data in HC_Clusters to characterize the cluster centers. If your answer is zero enter "0".
Cluster Size Female Married Loans Mortgage Characteristics
1
2
3
(b) Repeat part a, but use Jaccard’s coefficient as the similarity measure. How would you characterize each cluster? If your answer is zero enter "0".
Cluster Size Female Married Loans Mortgage Characteristics
1
2
3
(c) Compare the clusters and explain your observations.
(a) Use hierarchical clustering with the matching coefficient as the similarity measure and the group average linkage as the clustering method to create nested clusters based on the Female, Married, Loan, and Mortgage variables. Specify the construction of 3 clusters. How would you characterize each cluster? Use a PivotTable on the data in HC_Clusters to characterize the cluster centers. If your answer is zero enter "0".
Cluster Size Female Married Loans Mortgage Characteristics
1. All females with loans and mortgagesMales and females, mostly married, no loansAll males with loans, mostly marriedItem 6
2. All females with loans and mortgagesMales and females, mostly married, no loansAll males with loans, mostly marriedItem 12
3. All females with loans and mortgagesMales and females, mostly married, no loansAll males with loans, mostly marriedItem 18
(b) Repeat part a, but use Jaccard’s coefficient as the similarity measure. How would you characterize each cluster? If your answer is zero enter "0".
Cluster Size Female Married Loans Mortgage Characteristics
1. An unmarried male with loan but no mortgageMales and females, mostly married with loans and mortgagesAn unmarried male with no loan and mortgageItem 24
2. An unmarried male with loan but no mortgageMales and females, mostly married with loans and mortgagesAn unmarried male with no loan and mortgageItem 30
3. An unmarried male with loan but no mortgageMales and females, mostly married with loans and mortgagesAn unmarried male with no loan and mortgageItem 36
(c) Compare the clusters and explain your observations.