Cluster Analysis - Non-heirarchical(KMeans Cluster)
Loy
Sunday, December 14, 2014
We have a supermarket data. We will group the customers so that we can target the right group to increase sales.
We load the data and describe the data.
library("dplyr")market <- tbl_df(market)
market## Source: local data frame [3,000 x 6]
##
## cust_id age Estimated_income recent_spends family_size
## 1 1 30 3300 7.716e+02 1
## 2 2 46 12454 1.289e+02 3
## 3 3 76 0 0.000e+00 1
## 4 4 38 3000 7.697e+01 3
## 5 5 39 2500 2.500e+03 1
## 6 6 24 750 7.500e+02 1
## 7 7 68 1 3.689e-01 1
## 8 8 38 13000 1.084e+04 3
## 9 9 62 0 0.000e+00 1
## 10 10 29 2231 2.213e+03 1
## .. ... ... ... ... ...
## Variables not shown: Avg_visits_permonth (int)We will see if there’re missing values in the data.
sapply(market, function(x) sum(is.na(x)))## cust_id age Estimated_income
## 0 0 0
## recent_spends family_size Avg_visits_permonth
## 0 0 0We’ll see into how many clusters we can group the observations. We can use a simple technique to figure out how many clusters to form.
sqrt(3000)/2## [1] 27.39We’ll drop the the variable ‘cust_id’ so that we can process the other variables for cluster analysis.
market2 <- market %>%
select(age:Avg_visits_permonth)
market2## Source: local data frame [3,000 x 5]
##
## age Estimated_income recent_spends family_size Avg_visits_permonth
## 1 30 3300 7.716e+02 1 4
## 2 46 12454 1.289e+02 3 3
## 3 76 0 0.000e+00 1 8
## 4 38 3000 7.697e+01 3 3
## 5 39 2500 2.500e+03 1 1
## 6 24 750 7.500e+02 1 5
## 7 68 1 3.689e-01 1 3
## 8 38 13000 1.084e+04 3 9
## 9 62 0 0.000e+00 1 3
## 10 29 2231 2.213e+03 1 6
## .. ... ... ... ... ...We’ll start cluster analysis process. We will see into how many clusters we can group the observations until there is almost equal observations across all the clusters. We try with 7 clusters first.
market2_cluster1 <- kmeans(market2, centers = 7)
market2_cluster1## K-means clustering with 7 clusters of sizes 1171, 10, 97, 994, 218, 507, 3
##
## Cluster means:
## age Estimated_income recent_spends family_size Avg_visits_permonth
## 1 51.51 4622.1 1381.6 1.752 5.440
## 2 51.90 36800.5 22150.9 1.800 6.600
## 3 55.56 19336.0 3633.7 2.258 5.814
## 4 53.16 617.9 176.5 1.385 5.667
## 5 48.89 9336.7 6972.7 2.179 5.381
## 6 53.37 9705.9 1139.0 2.024 5.489
## 7 52.00 101864.3 10441.2 4.000 8.333
##
## Clustering vector:
## [1] 1 6 4 1 1 4 4 5 4 1 1 4 4 4 6 6 3 1 1 6 4 6 1 1 1 5 4 1 3 4 1 6 4 1
## [35] 1 1 4 4 1 5 1 4 4 1 6 1 6 4 3 4 1 5 6 1 1 4 6 4 1 4 6 5 1 6 1 1 6 5
## [69] 4 5 4 1 4 5 1 1 6 5 1 4 1 5 4 1 1 6 6 1 1 4 1 4 4 1 1 3 1 1 3 4 4 1
## [103] 4 4 4 6 4 1 4 4 1 6 4 4 4 4 6 6 5 1 1 3 3 1 4 5 4 4 1 5 4 6 4 4 1 1
## [137] 4 5 1 1 1 6 6 1 1 1 1 4 5 1 1 1 4 1 6 1 4 1 1 4 4 1 1 1 1 1 5 1 6 1
## [171] 1 4 5 4 1 1 1 1 5 1 4 4 4 1 4 5 1 4 3 1 6 4 4 5 5 1 4 1 5 1 6 1 1 6
## [205] 1 6 4 1 6 1 4 1 4 4 3 4 1 5 1 6 1 4 1 4 1 1 6 4 1 1 5 6 4 1 6 4 4 1
## [239] 4 1 1 4 4 3 1 3 5 4 1 4 1 1 6 4 4 3 4 5 1 1 4 1 4 4 4 6 5 1 4 4 1 4
## [273] 4 4 1 4 1 1 6 4 1 1 6 6 4 4 1 1 6 1 4 5 4 1 4 6 6 3 1 1 1 1 4 4 3 5
## [307] 1 5 6 4 6 6 6 4 4 1 1 1 1 3 4 5 1 5 6 4 4 1 1 6 4 1 4 1 4 1 1 6 1 1
## [341] 1 1 1 5 1 1 6 1 1 4 4 4 6 6 6 1 4 6 1 4 4 1 4 1 1 5 4 1 1 4 1 4 4 6
## [375] 6 1 4 1 4 4 1 1 1 4 1 4 1 1 4 1 5 4 1 4 1 6 5 1 1 1 6 1 4 1 4 1 4 6
## [409] 6 5 1 1 6 1 5 4 6 1 1 6 1 1 1 6 5 6 5 1 4 1 3 1 4 6 6 1 6 1 1 4 3 1
## [443] 6 1 5 4 5 1 4 4 5 5 1 1 4 4 4 4 1 6 4 1 1 1 4 6 6 4 1 4 1 1 5 1 4 4
## [477] 4 4 1 1 1 1 6 1 1 1 6 4 1 1 4 6 4 1 4 6 1 1 4 1 1 6 4 4 1 1 6 1 4 6
## [511] 6 1 4 4 6 4 1 1 4 4 6 4 1 1 1 4 1 1 1 4 6 5 4 4 6 1 1 4 4 6 4 1 1 6
## [545] 1 4 4 6 6 4 4 2 5 6 4 4 1 1 4 1 5 4 4 4 4 4 6 1 6 5 1 6 6 4 4 4 6 4
## [579] 6 4 1 4 6 1 4 5 1 1 4 4 4 4 1 4 1 5 1 4 5 5 1 4 6 4 4 4 4 4 1 4 4 1
## [613] 1 1 1 4 1 1 4 4 4 1 1 1 1 1 1 1 4 4 3 1 4 6 1 6 1 1 5 4 1 1 1 5 4 5
## [647] 1 6 6 6 1 1 6 4 4 3 1 1 4 6 6 1 4 4 4 4 6 1 6 3 4 6 5 1 4 3 4 6 4 4
## [681] 4 4 3 1 6 6 1 1 1 5 1 4 1 1 1 4 4 1 3 6 1 4 6 1 4 4 4 1 3 5 1 6 1 1
## [715] 5 5 5 1 4 6 6 6 4 4 4 1 1 6 4 1 1 1 1 1 1 1 6 1 5 5 6 1 4 1 1 4 1 4
## [749] 1 4 4 3 1 5 1 4 4 4 4 5 1 6 1 4 1 1 1 1 1 1 3 1 4 6 1 4 6 1 4 1 1 1
## [783] 6 4 1 6 1 1 5 6 1 4 6 4 4 4 1 6 1 1 2 6 3 4 6 1 4 1 6 1 4 1 6 1 6 5
## [817] 1 6 1 4 1 6 4 1 4 4 6 1 4 1 6 1 1 6 1 6 4 1 4 1 1 5 4 4 4 4 4 4 6 1
## [851] 1 4 1 1 4 1 4 1 1 6 6 4 4 5 1 4 1 6 1 1 1 1 1 5 4 6 1 1 6 1 1 4 1 4
## [885] 1 5 6 4 4 6 1 1 6 6 4 1 1 1 4 4 4 1 4 4 1 1 5 4 1 1 6 1 4 4 1 1 5 4
## [919] 1 4 1 1 4 6 4 4 4 1 4 1 1 1 1 1 1 3 6 6 1 6 6 1 4 4 1 4 6 6 1 6 1 4
## [953] 6 5 4 3 5 1 1 1 4 4 6 1 1 1 6 4 4 4 4 1 1 4 1 6 6 4 6 6 4 1 6 5 4 1
## [987] 3 1 1 1 6 4 4 5 4 1 4 1 4 1 1 1 6 3 4 4 1 6 4 1 6 4 1 4 1 1 1 1 1 1
## [1021] 1 1 6 1 1 6 1 1 3 4 4 4 4 4 4 3 4 4 6 4 3 6 1 4 4 4 1 1 4 6 1 5 6 6
## [1055] 1 4 1 4 4 4 6 6 4 6 1 5 4 4 4 1 4 4 4 4 1 1 6 4 4 6 4 1 6 4 6 1 5 1
## [1089] 1 4 6 1 3 1 4 6 1 6 6 1 1 1 4 4 6 4 1 4 1 4 6 6 6 4 1 4 4 4 1 4 1 1
## [1123] 1 4 3 4 4 4 4 6 4 4 6 6 1 1 5 1 4 1 6 1 1 1 6 4 1 6 6 1 1 1 6 4 4 6
## [1157] 1 4 1 6 1 1 4 4 6 4 5 4 1 3 4 6 1 4 1 4 1 4 1 1 4 4 1 1 6 4 6 1 1 4
## [1191] 4 1 4 4 1 6 4 4 1 1 4 1 1 1 4 6 4 1 4 4 4 6 1 5 1 4 1 4 6 4 4 4 4 5
## [1225] 4 4 1 6 1 1 4 4 5 6 5 1 4 1 1 6 5 1 4 4 1 1 1 6 4 4 1 4 1 3 4 1 6 1
## [1259] 5 5 4 3 4 1 1 5 4 1 6 6 4 3 4 1 4 6 1 4 1 5 3 4 1 1 1 6 1 4 4 1 6 1
## [1293] 3 4 1 1 1 4 4 4 4 1 1 4 6 1 6 4 5 4 1 1 1 4 6 4 6 6 1 4 1 1 1 1 5 5
## [1327] 1 6 6 1 4 4 6 6 5 1 5 3 4 4 4 5 5 1 1 1 6 1 5 4 1 4 1 6 5 4 4 5 4 1
## [1361] 4 4 1 4 1 1 4 4 5 6 4 4 5 1 1 4 6 5 6 6 1 6 1 1 1 4 1 4 1 1 1 6 1 1
## [1395] 6 4 1 1 1 1 6 4 4 1 4 4 1 4 4 1 4 4 1 6 4 1 4 5 1 1 1 6 4 5 1 1 4 4
## [1429] 6 1 1 6 6 1 5 4 4 1 1 1 6 4 4 4 1 1 1 1 1 1 6 1 1 1 1 6 6 5 1 4 3 3
## [1463] 1 6 1 6 1 1 4 6 6 1 6 4 5 4 3 4 4 1 4 4 4 6 4 4 4 4 1 4 1 1 1 4 4 4
## [1497] 1 6 1 1 6 6 4 4 1 4 5 6 6 4 1 6 1 1 1 4 4 1 6 4 6 4 6 1 1 1 4 6 6 1
## [1531] 1 6 1 4 5 4 4 1 1 1 1 5 4 4 3 6 5 1 4 4 6 1 4 1 6 4 5 5 1 4 1 4 1 4
## [1565] 6 6 4 4 4 4 1 1 1 5 6 1 4 1 4 4 1 3 5 4 4 1 1 6 4 1 1 3 1 1 4 6 4 4
## [1599] 1 1 4 4 5 1 5 1 5 5 4 1 1 1 4 4 6 1 4 6 4 1 4 1 5 1 1 4 1 4 1 1 6 1
## [1633] 4 5 1 1 1 4 1 4 1 4 1 1 1 5 1 4 6 4 1 1 4 6 1 4 6 1 4 4 1 6 1 5 4 4
## [1667] 4 1 4 4 4 3 5 1 5 4 5 4 1 4 5 1 4 1 1 1 6 4 1 4 1 6 1 1 6 1 1 1 4 4
## [1701] 1 6 6 1 1 1 1 4 6 4 5 4 3 5 1 1 4 3 1 1 1 1 1 4 1 1 6 6 4 1 6 1 1 4
## [1735] 1 1 1 4 3 3 6 4 1 1 5 1 6 1 1 6 6 5 1 2 4 6 1 5 6 4 5 4 1 6 1 1 1 6
## [1769] 4 6 4 4 1 1 1 1 6 4 4 6 6 1 1 1 4 6 1 4 4 1 1 4 4 4 4 4 1 4 1 1 4 3
## [1803] 4 1 1 4 4 4 6 4 4 3 1 1 1 1 1 1 1 1 6 4 1 1 4 4 1 4 4 1 6 4 3 1 1 6
## [1837] 1 3 1 4 6 4 6 4 6 1 4 6 1 6 4 4 1 4 4 3 1 1 1 3 6 4 4 4 1 6 5 1 4 1
## [1871] 1 6 4 1 1 4 4 1 6 4 1 6 6 4 1 6 5 4 1 4 1 4 4 1 5 5 6 4 1 1 6 6 4 4
## [1905] 6 1 4 6 1 1 1 1 1 5 6 5 4 4 6 4 1 1 5 1 6 4 1 1 1 4 6 4 3 6 1 4 1 1
## [1939] 4 1 4 6 4 4 1 1 1 1 1 1 1 2 4 4 1 2 3 1 4 6 4 4 4 4 4 4 6 4 4 1 1 1
## [1973] 1 6 1 4 1 1 1 1 5 4 5 6 6 4 4 4 6 5 1 1 4 1 4 1 4 4 1 1 1 1 1 4 1 6
## [2007] 6 6 4 1 1 1 6 6 5 4 6 6 1 5 4 4 4 6 1 1 5 1 1 1 1 4 1 1 1 4 3 1 3 1
## [2041] 1 4 6 1 4 4 1 4 1 4 1 1 1 4 4 1 6 1 6 1 6 6 4 6 1 4 4 4 1 1 1 1 3 1
## [2075] 6 4 3 1 6 5 4 4 4 5 1 1 6 1 1 5 1 4 5 6 1 4 4 1 1 4 4 1 4 1 1 6 4 6
## [2109] 4 1 3 4 4 6 4 4 1 4 5 4 5 1 4 1 6 4 6 1 3 4 4 4 1 6 1 1 1 1 6 1 3 4
## [2143] 1 4 1 6 4 4 1 4 1 7 4 1 4 4 1 4 4 6 6 5 1 1 1 5 5 6 2 1 4 6 1 4 1 3
## [2177] 6 1 4 1 6 6 4 5 1 3 1 6 1 5 1 5 4 1 6 4 1 4 4 4 4 4 1 4 5 4 1 6 1 4
## [2211] 3 1 1 1 6 1 6 4 4 1 5 6 1 1 1 4 1 5 6 1 1 4 1 1 4 4 4 4 6 4 4 1 5 6
## [2245] 4 4 3 4 1 4 6 4 3 1 6 4 4 4 1 4 6 1 1 1 6 5 1 1 1 4 4 1 1 5 1 4 1 3
## [2279] 1 6 1 4 5 1 1 1 1 4 1 6 4 4 6 6 4 4 4 1 4 6 4 4 1 4 1 4 6 1 6 1 4 1
## [2313] 1 1 4 4 4 1 1 4 1 4 6 1 1 5 1 1 6 1 1 1 1 1 5 5 1 1 4 1 1 4 4 5 6 1
## [2347] 1 4 4 1 1 4 1 6 4 4 7 4 4 4 1 1 1 1 1 6 3 1 4 6 6 1 4 5 4 6 1 4 1 6
## [2381] 6 6 1 4 1 4 1 6 1 1 4 1 6 1 6 4 4 4 5 4 6 1 6 6 1 4 6 1 1 4 1 1 1 1
## [2415] 1 4 1 5 6 1 5 4 4 4 4 1 1 1 1 6 4 4 3 4 6 7 4 4 4 6 4 1 4 3 4 1 6 1
## [2449] 5 4 1 1 4 1 1 4 4 5 1 1 1 5 1 6 4 1 4 1 1 4 6 6 4 4 4 5 4 6 1 1 4 4
## [2483] 4 1 1 4 1 4 6 4 1 6 6 1 4 6 1 4 5 6 4 6 5 1 4 6 6 6 4 1 5 1 6 3 6 1
## [2517] 2 4 1 6 6 3 1 1 5 1 1 6 4 1 4 6 6 4 6 1 4 6 5 4 1 4 1 6 3 1 4 6 5 4
## [2551] 1 4 4 1 1 4 5 1 5 1 4 1 6 4 4 3 6 4 3 4 4 2 4 1 4 5 1 1 6 6 4 1 5 1
## [2585] 5 6 4 6 1 1 4 4 6 4 3 6 4 4 4 1 4 5 3 5 4 6 6 1 1 6 1 4 3 1 1 5 5 3
## [2619] 4 4 1 4 6 1 1 1 4 4 4 1 1 4 4 6 4 4 5 1 4 1 6 4 4 4 4 4 4 6 1 6 4 1
## [2653] 1 4 4 3 3 6 1 1 6 4 4 4 1 4 5 1 4 1 4 6 6 1 6 1 4 6 1 6 1 4 6 4 1 4
## [2687] 1 1 1 1 1 4 1 4 1 4 6 4 1 4 6 4 1 6 4 4 4 1 6 1 1 1 5 5 4 6 3 1 6 4
## [2721] 5 1 1 4 4 6 6 1 4 4 4 4 6 1 6 1 4 6 6 1 4 3 4 1 4 4 4 1 6 1 1 6 4 1
## [2755] 1 6 1 1 1 1 4 1 4 4 1 1 5 3 5 4 1 4 4 4 6 4 4 3 1 4 5 6 1 1 1 4 1 6
## [2789] 3 4 4 1 1 4 1 1 1 4 1 1 4 1 6 4 6 2 1 1 1 1 4 6 1 6 6 1 6 1 1 4 5 4
## [2823] 4 1 6 1 6 1 1 6 1 4 6 1 4 3 1 1 6 4 1 1 5 4 1 4 6 4 4 1 1 4 4 6 4 4
## [2857] 1 6 4 1 4 5 5 6 1 1 4 1 1 4 5 1 5 4 1 4 1 5 1 1 1 4 1 1 4 4 1 1 4 4
## [2891] 1 1 6 1 1 4 4 1 4 1 4 4 1 4 1 4 1 1 1 1 5 1 6 1 6 3 1 4 1 4 1 4 4 4
## [2925] 1 4 1 1 4 4 4 4 1 2 1 5 4 4 4 4 4 1 4 6 1 4 1 1 4 6 4 6 1 1 6 4 1 1
## [2959] 5 4 1 4 1 1 5 6 4 4 1 5 4 6 4 4 3 1 6 1 1 4 4 1 5 6 6 4 5 4 4 4 1 6
## [2993] 1 1 6 5 4 4 1 4
##
## Within cluster sum of squares by cluster:
## [1] 4.061e+09 2.422e+09 3.472e+09 1.047e+09 2.516e+09 2.458e+09 2.815e+09
## (between_SS / total_SS = 84.9 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"market2_cluster2 <- kmeans(market2, centers = 5)
market2_cluster2## K-means clustering with 5 clusters of sizes 653, 111, 1210, 1021, 5
##
## Cluster means:
## age Estimated_income recent_spends family_size Avg_visits_permonth
## 1 52.04 9834.8 2745.4 2.113 5.449
## 2 54.39 19835.9 6504.6 2.207 5.676
## 3 51.65 4801.6 1392.3 1.745 5.445
## 4 53.04 669.9 199.9 1.399 5.676
## 5 53.40 82838.6 18498.8 3.000 8.600
##
## Clustering vector:
## [1] 3 1 4 3 3 4 4 2 4 4 3 4 4 4 1 1 2 3 3 1 4 1 3 3 3 1 4 3 2 4 3 1 4 3
## [35] 3 3 4 4 3 2 3 4 4 3 1 3 1 4 2 4 3 1 1 3 3 4 1 4 3 4 1 1 3 1 3 3 1 1
## [69] 4 2 4 3 4 1 3 3 1 3 3 4 3 1 4 3 3 1 1 3 3 4 3 4 4 3 3 2 3 3 2 4 4 3
## [103] 4 4 4 1 4 3 4 4 3 1 4 4 4 4 1 1 1 3 3 2 2 3 4 1 4 4 3 1 4 1 4 4 3 3
## [137] 4 1 3 3 3 1 3 3 3 3 3 4 1 3 3 3 4 3 1 3 4 3 3 4 4 3 3 3 3 3 1 3 1 3
## [171] 3 4 1 4 3 3 3 3 1 3 4 4 4 3 4 1 3 4 2 3 1 4 4 1 1 3 4 3 1 3 1 3 3 1
## [205] 3 1 4 3 1 3 4 4 4 4 2 4 3 1 3 1 3 4 3 4 3 3 1 4 3 3 2 1 4 3 1 4 4 3
## [239] 4 3 3 4 4 2 3 2 1 4 3 4 3 3 1 4 4 1 4 1 3 3 4 3 4 4 4 1 1 3 4 4 3 4
## [273] 4 4 3 4 3 4 1 4 3 3 1 3 4 4 3 3 1 3 4 1 4 3 4 1 1 2 3 3 3 3 4 4 2 1
## [307] 3 1 1 4 1 1 1 4 4 3 3 3 3 1 4 1 3 1 1 4 4 3 3 1 4 3 4 3 4 3 3 1 3 3
## [341] 3 3 3 1 3 3 3 3 3 4 4 4 1 1 1 3 4 1 3 4 4 3 4 3 3 1 4 4 3 4 3 4 4 3
## [375] 3 3 4 3 4 4 3 3 3 4 3 4 3 3 4 3 1 4 3 4 3 1 3 3 3 3 1 1 4 3 4 3 4 3
## [409] 1 1 3 3 1 3 1 4 1 3 3 1 3 3 3 3 1 1 1 3 4 3 1 3 4 1 1 3 1 3 3 4 2 3
## [443] 1 3 1 4 1 3 4 4 1 1 3 3 4 4 4 4 3 1 4 3 3 3 4 1 1 4 3 4 3 3 1 3 4 4
## [477] 4 4 3 3 3 3 3 3 3 3 1 4 4 3 4 1 4 3 4 1 3 3 4 3 3 1 4 4 3 3 1 3 4 1
## [511] 1 3 4 4 1 4 3 4 4 4 1 4 3 3 3 4 3 3 3 4 3 1 4 4 1 3 3 4 4 1 4 3 4 1
## [545] 3 4 4 1 1 4 4 5 2 1 4 4 3 3 4 3 1 4 4 4 4 4 1 3 1 1 3 1 1 4 4 4 1 4
## [579] 1 4 1 4 1 3 4 1 3 3 4 4 4 4 3 4 3 1 3 4 1 1 3 4 1 4 4 4 4 4 3 4 4 3
## [613] 3 3 3 4 3 3 4 4 4 3 3 3 3 3 3 3 4 4 2 3 4 1 3 1 3 3 1 4 3 3 3 1 4 1
## [647] 3 1 1 1 3 3 1 4 4 2 3 3 4 3 1 3 4 4 4 4 1 3 1 2 4 1 1 3 4 2 4 1 4 4
## [681] 4 4 2 3 1 3 3 3 3 1 3 4 3 3 3 4 4 3 2 1 3 4 1 3 4 4 4 4 2 1 3 1 3 3
## [715] 1 1 1 3 4 1 1 1 4 4 4 3 3 1 4 3 3 3 3 3 3 3 1 3 1 3 1 3 4 3 3 4 3 4
## [749] 3 4 4 2 3 1 3 4 4 4 4 3 3 1 3 4 3 4 3 3 3 3 2 3 4 1 3 4 1 3 4 3 3 3
## [783] 1 4 3 1 1 3 2 1 3 4 1 4 4 4 3 1 3 3 2 1 2 4 1 3 4 3 1 3 4 3 1 3 1 2
## [817] 3 1 3 4 4 1 4 3 4 4 1 3 4 3 3 3 3 1 3 1 4 3 4 3 3 1 4 4 4 4 4 4 1 3
## [851] 3 4 3 3 4 3 4 3 3 1 1 4 4 1 3 4 3 1 3 3 3 3 3 1 4 1 3 3 1 3 3 4 3 4
## [885] 3 1 1 4 4 1 3 3 1 1 4 3 3 3 4 4 4 1 4 4 3 3 1 4 3 3 1 3 4 4 3 3 2 4
## [919] 3 4 3 3 4 1 4 4 4 3 4 3 3 3 3 3 3 2 1 3 3 1 1 3 4 4 3 4 1 1 3 1 3 4
## [953] 1 1 4 1 2 3 3 3 4 4 3 3 3 3 1 4 4 4 4 3 3 4 3 1 1 4 1 1 4 3 1 1 4 3
## [987] 1 3 3 3 1 4 4 1 4 1 4 3 4 3 3 3 1 1 4 4 3 1 4 3 1 4 3 4 3 3 3 3 3 3
## [1021] 3 3 1 3 3 3 3 3 2 4 4 4 4 4 4 2 4 4 1 4 2 1 3 4 4 4 3 3 4 1 3 1 1 1
## [1055] 3 4 3 4 4 4 1 1 4 1 3 1 4 4 4 3 4 4 4 4 3 3 1 4 4 1 4 3 1 4 1 3 1 3
## [1089] 3 4 1 3 2 3 4 3 3 1 1 3 3 3 4 4 1 4 3 4 3 4 1 3 1 4 3 4 4 4 3 4 3 3
## [1123] 3 4 2 4 4 4 4 1 4 4 1 1 3 3 1 3 4 3 1 3 3 3 1 4 3 1 1 3 3 3 1 4 4 1
## [1157] 3 4 3 1 3 3 4 4 1 4 1 4 3 2 4 1 3 4 3 4 3 4 3 3 4 4 3 3 1 4 1 3 3 4
## [1191] 4 3 4 4 3 1 4 4 3 3 4 3 3 3 4 1 4 3 4 4 4 1 3 1 3 4 3 4 1 4 4 4 4 1
## [1225] 4 4 3 1 3 3 4 4 1 1 1 3 4 3 3 1 1 3 4 4 3 3 3 1 4 4 3 4 3 1 4 3 1 3
## [1259] 1 1 4 1 4 3 3 1 4 3 1 1 4 2 4 3 4 3 3 4 3 1 2 4 3 3 3 1 3 4 4 3 1 3
## [1293] 1 4 3 3 3 4 4 4 4 3 3 4 1 3 1 4 1 4 3 3 3 4 1 4 1 1 3 4 3 3 3 3 2 1
## [1327] 3 1 1 3 4 4 1 1 1 3 1 2 4 4 4 1 2 3 3 3 1 3 1 4 3 4 3 1 1 4 4 1 4 3
## [1361] 4 4 3 4 3 3 4 4 1 1 4 4 1 3 3 4 1 1 1 1 3 1 3 3 3 4 3 4 3 3 3 1 3 3
## [1395] 3 4 3 3 3 3 1 4 4 3 4 4 4 4 4 3 4 4 3 1 4 3 4 1 3 3 3 1 4 1 3 3 4 4
## [1429] 1 3 3 3 1 3 1 4 4 3 3 3 3 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 1 1 4 4 2 2
## [1463] 1 1 3 1 3 3 4 1 3 3 1 4 1 4 2 4 4 3 4 4 4 1 4 4 4 4 3 4 3 3 3 4 4 4
## [1497] 3 1 3 3 1 1 4 4 3 4 1 1 1 4 3 1 3 3 3 4 4 3 1 4 3 4 3 3 3 3 4 1 1 3
## [1531] 3 1 3 4 1 4 4 3 3 3 3 1 4 4 1 1 3 3 4 4 3 3 4 3 1 4 1 2 3 4 3 4 3 4
## [1565] 1 1 4 4 4 4 3 3 3 1 1 3 4 3 4 4 3 2 3 4 4 3 3 1 4 3 4 2 3 3 4 1 4 4
## [1599] 3 3 4 4 1 3 1 3 1 2 4 3 3 3 4 4 1 3 4 1 4 3 4 3 1 3 3 4 3 4 3 3 1 3
## [1633] 4 1 3 3 3 4 3 4 3 4 3 3 3 1 3 4 1 4 4 3 4 1 3 4 1 3 4 4 3 1 3 1 4 4
## [1667] 4 3 4 4 4 2 2 3 1 4 2 4 1 4 1 3 4 3 3 3 1 4 3 4 3 1 3 3 1 3 3 3 4 4
## [1701] 3 1 3 3 3 3 3 4 3 4 3 4 2 1 3 3 4 2 3 3 3 3 3 4 3 3 1 1 4 3 1 3 3 4
## [1735] 3 3 3 4 1 2 1 4 3 3 1 3 1 3 3 1 3 1 3 2 4 1 3 1 1 4 1 4 3 1 3 3 3 1
## [1769] 4 1 4 4 3 3 3 3 1 4 4 1 1 3 3 3 4 1 3 4 4 3 3 4 4 4 4 4 3 4 3 3 4 2
## [1803] 4 3 3 4 4 4 1 4 4 2 3 3 3 3 3 3 3 3 3 4 3 3 4 4 3 4 4 3 1 4 2 3 3 1
## [1837] 3 2 1 4 1 4 1 4 1 3 4 3 3 1 4 4 3 4 4 2 3 3 3 2 1 4 4 4 3 3 1 3 4 3
## [1871] 3 1 4 3 3 4 4 3 1 4 3 1 1 4 3 1 3 4 3 4 3 4 4 3 1 1 1 4 3 3 1 1 4 4
## [1905] 1 3 4 1 3 3 3 3 3 1 1 3 4 4 1 4 3 3 1 3 1 4 3 3 4 4 1 4 2 3 3 4 3 3
## [1939] 4 3 4 1 4 4 3 3 3 3 3 3 3 5 4 4 3 2 2 3 4 1 4 4 4 4 4 4 1 4 4 3 3 3
## [1973] 3 1 3 4 3 3 3 3 1 4 1 1 1 4 4 4 3 1 3 3 4 3 4 3 4 4 3 3 3 3 3 4 3 1
## [2007] 1 1 4 3 3 4 1 1 1 4 3 1 3 1 4 4 4 1 3 3 1 3 3 3 3 4 3 3 3 4 2 3 1 3
## [2041] 3 4 1 3 4 4 3 4 3 4 3 3 3 4 4 3 3 3 3 3 1 3 4 1 3 4 4 4 3 3 3 3 1 3
## [2075] 1 4 2 3 1 1 4 4 4 1 3 4 1 3 3 1 3 4 3 1 3 4 4 3 3 4 4 3 4 3 3 1 4 1
## [2109] 4 3 2 4 4 1 4 4 3 4 1 4 1 3 4 3 3 4 1 3 2 4 4 4 3 1 3 3 3 3 3 3 2 4
## [2143] 3 4 3 1 4 4 3 4 3 5 4 3 4 4 3 4 4 1 1 1 3 3 3 1 3 1 2 3 4 1 3 4 3 2
## [2177] 1 3 4 3 1 1 4 1 3 2 3 1 3 1 3 3 4 3 1 4 3 4 4 4 4 4 3 4 2 4 3 1 3 4
## [2211] 2 3 3 3 1 3 1 4 4 3 1 1 3 3 3 4 3 1 1 3 3 4 4 3 4 4 4 4 1 4 4 3 1 1
## [2245] 4 4 2 4 3 4 3 4 2 3 1 4 4 4 3 4 1 3 3 3 1 1 3 3 3 4 4 3 3 1 3 4 3 2
## [2279] 3 1 3 4 1 3 3 3 3 4 4 1 4 4 1 1 4 4 4 3 4 1 4 4 3 4 4 4 1 3 1 3 4 3
## [2313] 3 3 4 4 4 3 3 4 3 4 1 3 3 1 3 1 1 3 3 3 3 3 1 2 3 3 4 3 3 4 4 3 1 3
## [2347] 3 4 4 3 3 4 3 3 4 4 5 4 4 4 3 3 3 4 3 1 2 3 4 1 1 3 4 1 4 1 3 4 3 1
## [2381] 1 1 3 4 3 4 3 1 3 3 4 3 1 3 1 4 4 4 1 4 1 3 3 1 3 4 1 3 3 4 3 3 3 3
## [2415] 3 4 3 1 1 3 1 4 4 4 4 3 3 3 3 3 4 4 2 4 1 5 4 4 4 1 4 3 4 2 4 3 1 3
## [2449] 3 4 3 3 4 4 3 4 4 1 3 3 3 1 3 3 4 3 4 3 3 4 1 3 4 4 4 1 4 1 3 3 4 4
## [2483] 4 3 4 4 3 4 1 4 3 1 1 4 4 1 3 4 1 1 4 3 1 3 4 1 1 1 4 3 1 3 1 2 1 3
## [2517] 2 4 3 3 1 2 3 3 1 3 3 1 4 3 4 1 1 4 1 3 4 1 1 4 3 4 3 1 2 3 4 1 3 4
## [2551] 3 4 4 3 3 4 2 3 1 3 4 3 1 4 4 2 3 4 2 4 4 2 4 3 4 1 3 3 3 1 4 3 1 3
## [2585] 1 1 4 1 3 3 4 4 1 4 2 1 4 4 4 3 4 1 2 1 4 1 1 3 3 1 3 4 2 3 3 1 1 2
## [2619] 4 4 3 4 1 3 3 3 4 4 4 3 3 4 4 1 4 4 1 3 4 3 1 4 4 4 4 4 4 1 3 1 4 1
## [2653] 3 4 4 2 2 1 3 3 1 4 4 4 3 4 1 3 4 3 4 1 1 3 1 3 4 3 3 1 3 4 1 4 3 4
## [2687] 3 3 3 3 3 4 3 4 3 4 1 4 3 4 1 4 3 3 4 4 4 3 1 3 3 3 1 3 4 1 2 3 1 4
## [2721] 1 3 3 4 4 1 1 3 4 4 4 4 1 3 1 4 4 1 1 3 4 2 4 3 4 4 4 3 1 3 3 1 4 3
## [2755] 3 1 3 3 3 3 4 3 4 4 3 3 2 2 3 4 3 4 4 4 1 4 4 2 3 4 3 3 3 3 3 4 3 1
## [2789] 2 4 4 3 3 4 3 3 3 4 3 3 4 4 1 4 1 2 3 3 3 3 4 1 3 1 3 3 3 3 3 4 1 4
## [2823] 4 3 1 3 1 3 3 1 3 4 1 3 4 2 3 3 1 4 3 3 2 4 3 4 1 4 4 3 3 4 4 1 4 4
## [2857] 3 1 4 3 4 1 1 1 3 3 4 3 3 4 3 3 1 4 3 4 3 1 3 3 3 4 3 3 4 4 3 3 4 4
## [2891] 3 3 1 3 3 4 4 3 4 3 4 4 4 4 3 4 3 3 3 3 1 3 1 3 1 1 3 4 3 4 3 4 4 4
## [2925] 3 4 3 3 4 4 4 4 3 2 3 1 4 4 4 4 4 3 4 1 3 4 3 3 4 1 4 1 3 3 1 4 3 3
## [2959] 1 4 3 4 3 3 1 1 4 4 3 3 4 1 4 4 2 3 1 3 3 4 4 3 1 1 1 4 1 4 4 4 3 1
## [2993] 3 3 1 1 4 4 3 4
##
## Within cluster sum of squares by cluster:
## [1] 7.620e+09 9.052e+09 4.698e+09 1.189e+09 6.152e+09
## (between_SS / total_SS = 77.0 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"market2_cluster3 <- kmeans(market2, centers = 4)
market2_cluster3## K-means clustering with 4 clusters of sizes 250, 7, 1237, 1506
##
## Cluster means:
## age Estimated_income recent_spends family_size Avg_visits_permonth
## 1 53.61 15500 5210.4 2.236 5.432
## 2 54.86 72436 17881.6 2.571 8.429
## 3 51.57 7081 1917.0 1.914 5.475
## 4 52.69 1588 462.8 1.483 5.594
##
## Clustering vector:
## [1] 4 3 4 4 4 4 4 1 4 4 4 4 4 4 3 3 1 4 3 3 4 3 4 4 3 1 4 4 1 4 4 3 4 4
## [35] 3 4 4 4 3 1 3 4 4 4 3 3 3 4 1 4 3 3 3 3 3 4 3 4 3 4 1 3 3 3 4 4 3 3
## [69] 4 1 4 3 4 3 3 4 3 3 3 4 3 1 4 3 3 3 3 3 4 4 3 4 4 3 4 1 4 4 1 4 4 3
## [103] 4 4 4 3 4 3 4 4 3 3 4 4 4 4 3 3 3 3 4 1 1 3 4 3 4 4 4 1 4 3 4 4 4 4
## [137] 4 1 4 3 4 3 3 3 4 4 3 4 3 3 4 3 4 3 3 3 4 3 4 4 4 4 4 3 3 4 3 3 3 3
## [171] 4 4 3 4 3 3 4 3 1 3 4 4 4 3 4 1 4 4 1 3 3 4 4 3 1 3 4 3 1 3 3 3 3 3
## [205] 3 3 4 4 3 4 4 4 4 4 1 4 3 3 4 3 3 4 3 4 3 3 3 4 3 4 1 3 4 4 3 4 4 3
## [239] 4 4 4 4 4 1 3 1 1 4 3 4 3 4 3 4 4 1 4 1 3 4 4 4 4 4 4 3 3 4 4 4 3 4
## [273] 4 4 4 4 4 4 3 4 3 3 3 3 4 4 4 3 3 3 4 3 4 4 4 1 3 1 3 3 3 4 4 4 1 3
## [307] 4 3 3 4 3 3 3 4 4 3 4 4 4 1 4 1 3 1 3 4 4 4 4 3 4 3 4 3 4 3 4 3 3 3
## [341] 4 4 4 1 4 4 3 3 3 4 4 4 3 3 3 4 4 3 4 4 4 3 4 4 4 3 4 4 4 4 3 4 4 3
## [375] 3 3 4 4 4 4 3 3 3 4 4 4 3 4 4 3 3 4 4 4 3 3 3 3 3 3 3 3 4 3 4 4 4 3
## [409] 3 3 4 4 3 3 1 4 3 3 3 3 3 3 3 3 3 3 1 3 4 3 1 4 4 3 3 4 3 4 3 4 1 4
## [443] 3 3 3 4 1 4 4 4 1 1 3 4 4 4 4 4 3 3 4 4 3 4 4 3 3 4 3 4 4 3 3 4 4 4
## [477] 4 4 4 3 3 3 3 3 4 3 3 4 4 3 4 3 4 3 4 3 3 4 4 4 4 3 4 4 3 3 1 4 4 3
## [511] 3 4 4 4 3 4 4 4 4 4 3 4 3 4 4 4 4 3 3 4 3 3 4 4 3 3 4 4 4 3 4 3 4 1
## [545] 4 4 4 3 1 4 4 2 1 3 4 4 3 3 4 3 3 4 4 4 4 4 1 4 1 3 4 3 3 4 4 4 3 4
## [579] 3 4 3 4 3 3 4 3 4 3 4 4 4 4 4 4 3 1 3 4 1 3 4 4 3 4 4 4 4 4 4 4 4 4
## [613] 3 4 4 4 4 4 4 4 4 3 3 3 3 4 3 3 4 4 1 3 4 3 4 1 3 4 3 4 3 3 4 1 4 1
## [647] 3 3 3 3 3 3 1 4 4 1 3 4 4 3 3 3 4 4 4 4 3 3 3 1 4 3 3 4 4 1 4 3 4 4
## [681] 4 4 1 3 3 3 3 3 3 1 4 4 3 4 3 4 4 3 1 3 3 4 3 3 4 4 4 4 1 3 3 3 3 3
## [715] 1 3 3 4 4 3 1 3 4 4 4 3 3 3 4 4 4 3 4 3 4 3 3 3 3 3 3 3 4 3 3 4 4 4
## [749] 4 4 4 1 3 3 3 4 4 4 4 3 4 3 3 4 4 4 3 3 4 3 1 3 4 3 4 4 3 3 4 4 3 4
## [783] 3 4 3 3 3 3 1 3 3 4 3 4 4 4 3 1 4 4 1 3 1 4 3 3 4 3 3 3 4 3 3 4 1 1
## [817] 3 3 4 4 4 3 4 3 4 4 3 3 4 3 3 3 3 1 4 3 4 3 4 3 3 1 4 4 4 4 4 4 3 4
## [851] 3 4 3 4 4 3 4 4 3 1 3 4 4 3 4 4 3 3 4 4 3 3 4 3 4 3 3 4 3 3 4 4 3 4
## [885] 3 3 3 4 4 3 4 3 3 1 4 3 4 3 4 4 4 3 4 4 3 3 1 4 3 4 3 3 4 4 3 3 1 4
## [919] 4 4 4 4 4 3 4 4 4 4 4 3 3 4 3 4 3 1 3 3 4 3 3 4 4 4 4 4 3 3 3 3 4 4
## [953] 3 1 4 1 1 3 3 4 4 4 3 4 3 3 3 4 4 4 4 3 3 4 4 1 3 4 1 1 4 3 3 3 4 4
## [987] 1 3 3 3 1 4 4 3 4 3 4 4 4 4 4 3 3 1 4 4 4 3 4 3 3 4 3 4 3 3 3 4 3 3
## [1021] 3 3 3 3 4 3 3 4 1 4 4 4 4 4 4 1 4 4 3 4 1 3 4 4 4 4 3 3 4 3 3 3 3 3
## [1055] 3 4 4 4 4 4 3 3 4 3 4 3 4 4 4 3 4 4 4 4 4 3 1 4 4 3 4 4 3 4 3 4 1 3
## [1089] 3 4 3 3 1 3 4 3 3 1 3 4 3 3 4 4 3 4 3 4 4 4 1 3 3 4 3 4 4 4 4 4 3 3
## [1123] 3 4 1 4 4 4 4 3 4 4 3 3 3 3 1 3 4 3 1 3 3 4 3 4 3 3 3 3 3 3 3 4 4 3
## [1157] 3 4 4 3 4 3 4 4 3 4 3 4 4 1 4 3 3 4 4 4 4 4 3 3 4 4 3 3 1 4 3 4 3 4
## [1191] 4 4 4 4 3 3 4 4 3 4 4 4 4 3 4 3 4 4 4 4 4 3 3 3 3 4 3 4 3 4 4 4 4 3
## [1225] 4 4 3 3 3 3 4 4 3 3 3 4 4 4 4 3 3 3 4 4 3 3 4 3 4 4 3 4 4 1 4 4 1 4
## [1259] 1 3 4 1 4 4 4 1 4 3 3 1 4 1 4 3 4 3 3 4 4 3 1 4 4 4 4 1 3 4 4 4 3 3
## [1293] 1 4 4 4 4 4 4 4 4 3 4 4 3 4 3 4 3 4 3 3 3 4 3 4 3 3 3 4 4 3 4 3 1 3
## [1327] 3 3 3 4 4 4 3 3 3 4 1 1 4 4 4 3 1 3 4 3 3 4 3 4 3 4 3 3 3 4 4 1 4 3
## [1361] 4 4 4 4 3 4 4 4 3 1 4 4 3 3 3 4 3 1 3 3 3 3 3 4 4 4 4 4 4 4 3 3 4 3
## [1395] 3 4 4 4 3 4 3 4 4 3 4 4 4 4 4 4 4 4 4 1 4 3 4 1 3 4 3 1 4 3 3 3 4 4
## [1429] 1 4 4 3 3 3 3 4 4 4 3 4 3 4 4 4 3 4 3 4 4 3 3 4 3 3 4 3 3 1 4 4 1 1
## [1463] 3 3 3 1 4 4 4 3 3 4 3 4 1 4 1 4 4 3 4 4 4 1 4 4 4 4 3 4 3 3 3 4 4 4
## [1497] 3 3 4 3 3 3 4 4 3 4 1 3 3 4 3 3 4 3 3 4 4 4 1 4 3 4 3 3 3 3 4 3 3 4
## [1531] 4 3 3 4 3 4 4 3 4 3 3 3 4 4 1 3 3 3 4 4 3 3 4 3 3 4 3 1 4 4 3 4 3 4
## [1565] 1 3 4 4 4 4 4 3 4 3 3 4 4 3 4 4 3 1 3 4 4 3 3 3 4 3 4 1 3 3 4 3 4 4
## [1599] 3 3 4 4 3 3 3 4 3 1 4 4 3 3 4 4 1 4 4 3 4 3 4 3 1 4 4 4 4 4 4 4 1 4
## [1633] 4 1 3 4 4 4 4 4 3 4 4 4 4 1 3 4 3 4 4 4 4 3 3 4 3 3 4 4 3 3 3 3 4 4
## [1667] 4 3 4 4 4 1 1 3 3 4 1 4 3 4 3 3 4 3 4 4 3 4 4 4 4 3 3 3 3 4 4 4 4 4
## [1701] 4 3 3 3 3 3 3 4 3 4 3 4 1 1 3 3 4 1 4 3 3 3 3 4 4 3 1 3 4 4 3 3 4 4
## [1735] 4 3 4 4 1 1 3 4 3 4 3 3 3 3 4 3 3 3 3 1 4 3 4 1 3 4 3 4 3 3 3 3 3 3
## [1769] 4 3 4 4 3 3 4 3 3 4 4 3 1 4 3 3 4 3 4 4 4 4 4 4 4 4 4 4 4 4 3 3 4 1
## [1803] 4 4 4 4 4 4 1 4 4 1 3 4 4 3 3 3 3 3 3 4 4 3 4 4 3 4 4 4 3 4 1 4 4 1
## [1837] 4 1 3 4 3 4 3 4 3 4 4 3 4 3 4 4 3 4 4 1 4 3 3 1 3 4 4 4 3 3 3 3 4 3
## [1871] 4 3 4 3 4 4 4 4 1 4 4 1 3 4 4 3 3 4 3 4 4 4 4 3 3 1 3 4 4 3 1 3 4 4
## [1905] 3 4 4 1 3 3 4 4 3 1 3 3 4 4 3 4 4 3 1 3 3 4 3 3 4 4 3 4 1 3 3 4 3 3
## [1939] 4 4 4 3 4 4 3 4 4 4 3 4 3 2 4 4 4 1 1 3 4 3 4 4 4 4 4 4 3 4 4 3 3 4
## [1973] 3 3 4 4 3 3 4 4 1 4 3 3 1 4 4 4 3 1 3 3 4 4 4 3 4 4 3 3 4 3 3 4 3 3
## [2007] 1 3 4 4 4 4 3 1 3 4 3 3 3 3 4 4 4 3 4 3 3 3 4 3 3 4 3 4 3 4 1 4 1 3
## [2041] 4 4 3 4 4 4 4 4 3 4 3 4 3 4 4 3 3 4 3 4 3 3 4 3 4 4 4 4 4 4 4 3 1 4
## [2075] 3 4 1 4 3 3 4 4 4 3 3 4 1 3 4 3 4 4 3 3 4 4 4 3 4 4 4 4 4 3 4 3 4 3
## [2109] 4 3 1 4 4 1 4 4 3 4 1 4 3 3 4 3 3 4 3 4 1 4 4 4 3 3 3 4 3 3 3 3 1 4
## [2143] 3 4 4 3 4 4 4 4 3 2 4 3 4 4 3 4 4 3 3 1 3 3 3 3 3 3 1 4 4 3 4 4 3 1
## [2177] 1 3 4 3 3 3 4 1 4 1 4 1 3 3 4 3 4 4 3 4 4 4 4 4 4 4 3 4 1 4 3 3 4 4
## [2211] 1 3 3 4 3 3 3 4 4 4 3 3 3 3 4 4 4 1 3 3 3 4 4 3 4 4 4 4 3 4 4 3 1 1
## [2245] 4 4 1 4 3 4 3 4 1 4 3 4 4 4 4 4 3 3 3 4 3 3 4 4 3 4 4 3 3 3 3 4 3 1
## [2279] 3 3 3 4 3 4 3 3 3 4 4 3 4 4 3 3 4 4 4 3 4 3 4 4 3 4 4 4 3 4 3 3 4 4
## [2313] 4 3 4 4 4 3 4 4 3 4 3 4 4 3 3 3 3 3 4 3 4 3 1 1 3 3 4 3 3 4 4 3 3 4
## [2347] 3 4 4 3 4 4 3 3 4 4 2 4 4 4 4 4 3 4 3 3 1 4 4 3 3 3 4 3 4 3 3 4 3 3
## [2381] 3 3 3 4 4 4 3 3 4 4 4 4 3 4 3 4 4 4 3 4 1 3 3 3 4 4 3 3 3 4 3 3 3 4
## [2415] 4 4 4 1 3 3 3 4 4 4 4 4 3 3 3 3 4 4 1 4 3 2 4 4 4 3 4 4 4 1 4 4 3 3
## [2449] 3 4 3 3 4 4 3 4 4 1 3 3 3 3 3 3 4 4 4 3 3 4 3 3 4 4 4 3 4 3 3 4 4 4
## [2483] 4 3 4 4 4 4 3 4 4 3 3 4 4 3 3 4 1 3 4 3 1 3 4 1 3 3 4 4 3 3 3 1 3 4
## [2517] 2 4 4 3 3 1 4 3 3 4 4 3 4 4 4 3 3 4 3 4 4 3 3 4 3 4 4 3 1 3 4 3 3 4
## [2551] 4 4 4 4 3 4 1 4 3 3 4 3 3 4 4 1 3 4 1 4 4 2 4 3 4 3 3 3 3 1 4 4 3 3
## [2585] 3 1 4 3 3 3 4 4 3 4 1 3 4 4 4 3 4 1 1 3 4 3 3 3 4 3 4 4 1 3 3 3 3 1
## [2619] 4 4 3 4 3 4 3 4 4 4 4 3 4 4 4 3 4 4 1 4 4 3 3 4 4 4 4 4 4 3 3 3 4 3
## [2653] 4 4 4 1 1 3 3 3 3 4 4 4 4 4 1 4 4 3 4 3 3 4 3 3 4 3 3 3 3 4 3 4 4 4
## [2687] 3 3 4 4 3 4 3 4 3 4 3 4 4 4 3 4 4 3 4 4 4 3 3 3 4 4 3 3 4 3 1 4 3 4
## [2721] 3 3 3 4 4 3 3 4 4 4 4 4 3 3 3 4 4 3 1 3 4 1 4 4 4 4 4 3 3 3 4 3 4 3
## [2755] 3 3 4 4 4 4 4 3 4 4 4 4 1 1 3 4 4 4 4 4 1 4 4 1 4 4 3 3 4 3 3 4 3 3
## [2789] 1 4 4 3 3 4 3 3 3 4 4 4 4 4 3 4 1 1 3 3 3 4 4 3 3 3 3 3 3 4 4 4 3 4
## [2823] 4 3 3 4 3 4 4 3 4 4 3 4 4 1 3 4 3 4 3 4 1 4 4 4 1 4 4 4 3 4 4 3 4 4
## [2857] 4 1 4 3 4 3 1 3 3 3 4 3 4 4 3 3 3 4 4 4 4 3 4 4 3 4 4 4 4 4 4 4 4 4
## [2891] 4 4 3 3 3 4 4 4 4 3 4 4 4 4 4 4 3 3 4 3 3 4 3 4 1 1 3 4 3 4 3 4 4 4
## [2925] 3 4 3 4 4 4 4 4 3 1 4 3 4 4 4 4 4 3 4 3 4 4 3 4 4 3 4 1 3 3 3 4 4 4
## [2959] 3 4 3 4 3 4 3 3 4 4 4 3 4 3 4 4 1 3 3 3 4 4 4 3 1 3 3 4 3 4 4 4 4 3
## [2993] 4 4 3 3 4 4 3 4
##
## Within cluster sum of squares by cluster:
## [1] 1.219e+10 8.563e+09 9.959e+09 4.711e+09
## (between_SS / total_SS = 71.6 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"market2_cluster4 <- kmeans(market2, centers = 3)
market2_cluster4## K-means clustering with 3 clusters of sizes 885, 7, 2108
##
## Cluster means:
## age Estimated_income recent_spends family_size Avg_visits_permonth
## 1 52.27 10599 3108.9 2.079 5.480
## 2 54.86 72436 17881.6 2.571 8.429
## 3 52.32 2678 768.3 1.575 5.553
##
## Clustering vector:
## [1] 3 1 3 3 3 3 3 1 3 3 3 3 3 3 1 1 1 3 3 1 3 1 3 3 3 1 3 3 1 3 3 1 3 3
## [35] 3 3 3 3 3 1 3 3 3 3 1 3 1 3 1 3 3 1 1 1 3 3 1 3 3 3 1 1 3 1 3 3 1 1
## [69] 3 1 3 3 3 1 3 3 1 3 3 3 3 1 3 1 3 1 1 3 3 3 1 3 3 3 3 1 3 3 1 3 3 3
## [103] 3 3 3 1 3 3 3 3 1 1 3 3 3 3 1 1 1 3 3 1 1 3 3 1 3 3 3 1 3 1 3 3 3 3
## [137] 3 1 3 3 3 1 1 3 3 3 3 3 1 1 3 1 3 3 1 3 3 3 3 3 3 3 3 3 3 3 1 3 1 3
## [171] 3 3 1 3 3 3 3 3 1 3 3 3 3 1 3 1 3 3 1 3 1 3 3 1 1 3 3 3 1 3 1 3 3 1
## [205] 3 1 3 3 1 3 3 3 3 3 1 3 3 1 3 1 3 3 3 3 3 3 1 3 3 3 1 1 3 3 1 3 3 3
## [239] 3 3 3 3 3 1 3 1 1 3 3 3 3 3 1 3 3 1 3 1 3 3 3 3 3 3 3 1 1 3 3 3 3 3
## [273] 3 3 3 3 3 3 1 3 3 3 1 1 3 3 3 3 1 3 3 1 3 3 3 1 1 1 3 3 3 3 3 3 1 1
## [307] 3 1 1 3 1 1 1 3 3 3 3 3 3 1 3 1 3 1 1 3 3 3 3 1 3 3 3 3 3 1 3 1 3 3
## [341] 3 3 3 1 3 3 1 3 3 3 3 3 1 1 1 3 3 1 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 1
## [375] 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 3 1 1 3 3 3 1 1 3 3 3 3 3 1
## [409] 1 1 3 3 1 1 1 3 1 3 3 1 3 3 3 1 1 1 1 3 3 1 1 3 3 1 1 3 1 3 3 3 1 3
## [443] 1 3 1 3 1 3 3 3 1 1 3 3 3 3 3 3 3 1 3 3 3 3 3 1 1 3 3 3 3 3 1 3 3 3
## [477] 3 3 3 3 3 3 1 3 3 3 1 3 3 3 3 1 3 3 3 1 3 3 3 3 3 1 3 3 3 3 1 3 3 1
## [511] 1 3 3 3 1 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 1 1 3 3 1 3 3 3 3 1 3 3 3 1
## [545] 3 3 3 1 1 3 3 2 1 1 3 3 3 3 3 3 1 3 3 3 3 3 1 3 1 1 3 1 1 3 3 3 1 3
## [579] 1 3 1 3 1 3 3 1 3 3 3 3 3 3 3 3 3 1 3 3 1 1 3 3 1 3 3 3 3 3 3 3 3 3
## [613] 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 1 3 1 3 3 1 3 3 3 3 1 3 1
## [647] 3 1 1 1 3 3 1 3 3 1 3 3 3 1 1 3 3 3 3 3 1 3 1 1 3 1 1 3 3 1 3 1 3 3
## [681] 3 3 1 3 1 1 3 3 1 1 3 3 3 3 1 3 3 3 1 1 3 3 1 3 3 3 3 3 1 1 3 1 3 3
## [715] 1 1 1 3 3 1 1 1 3 3 3 3 3 1 3 3 3 3 3 3 3 3 1 3 1 1 1 3 3 3 3 3 3 3
## [749] 3 3 3 1 3 1 1 3 3 3 3 1 3 1 3 3 3 3 1 3 3 3 1 3 3 1 3 3 1 3 3 3 3 3
## [783] 1 3 1 1 1 1 1 1 3 3 1 3 3 3 3 1 3 3 1 1 1 3 1 3 3 3 1 3 3 1 1 3 1 1
## [817] 3 1 3 3 3 1 3 3 3 3 1 3 3 3 1 1 3 1 3 1 3 3 3 3 3 1 3 3 3 3 3 3 1 3
## [851] 3 3 3 3 3 3 3 3 3 1 1 3 3 1 3 3 3 1 3 3 3 3 3 1 3 1 3 3 1 3 3 3 3 3
## [885] 3 1 1 3 3 1 3 3 1 1 3 3 3 3 3 3 3 1 3 3 3 3 1 3 3 3 1 3 3 3 1 3 1 3
## [919] 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 1 1 1 3 1 1 3 3 3 3 3 1 1 3 1 3 3
## [953] 1 1 3 1 1 3 3 3 3 3 1 3 3 3 1 3 3 3 3 3 3 3 3 1 1 3 1 1 3 3 1 1 3 3
## [987] 1 3 3 3 1 3 3 1 3 1 3 3 3 3 3 3 1 1 3 3 3 1 3 3 1 3 1 3 3 1 3 3 3 3
## [1021] 3 3 1 3 3 1 3 3 1 3 3 3 3 3 3 1 3 3 1 3 1 1 3 3 3 3 3 3 3 1 3 1 1 1
## [1055] 3 3 3 3 3 3 1 1 3 1 3 1 3 3 3 3 3 3 3 3 3 3 1 3 3 1 3 3 1 3 1 3 1 3
## [1089] 1 3 1 3 1 3 3 1 3 1 1 3 3 3 3 3 1 3 3 3 3 3 1 1 1 3 3 3 3 3 3 3 1 1
## [1123] 3 3 1 3 3 3 3 1 3 3 1 1 3 3 1 3 3 3 1 3 3 3 1 3 3 1 1 3 3 3 1 3 3 1
## [1157] 3 3 3 1 3 3 3 3 1 3 1 3 3 1 3 1 3 3 3 3 3 3 3 3 3 3 3 3 1 3 1 3 3 3
## [1191] 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 1 3 1 3 3 3 3 1 3 3 3 3 1
## [1225] 3 3 3 1 3 1 3 3 1 1 1 3 3 3 3 1 1 3 3 3 3 3 3 1 3 3 3 3 3 1 3 3 1 3
## [1259] 1 1 3 1 3 3 3 1 3 3 1 1 3 1 3 1 3 1 3 3 3 1 1 3 3 3 3 1 3 3 3 3 1 3
## [1293] 1 3 3 3 3 3 3 3 3 3 3 3 1 3 1 3 1 3 3 3 3 3 1 3 1 1 3 3 3 3 3 1 1 1
## [1327] 3 1 1 3 3 3 1 1 1 3 1 1 3 3 3 1 1 3 3 3 1 3 1 3 3 3 3 1 1 3 3 1 3 3
## [1361] 3 3 3 3 3 3 3 3 1 1 3 3 1 3 3 3 1 1 1 1 3 1 3 3 3 3 3 3 3 3 3 1 3 3
## [1395] 1 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 3 1 3 3 3 1 3 1 3 3 3 3
## [1429] 1 3 3 1 1 3 1 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 1 3 3 3 3 1 1 1 3 3 1 1
## [1463] 1 1 3 1 3 3 3 1 3 3 1 3 1 3 1 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3
## [1497] 3 1 3 3 1 1 3 3 3 3 1 1 1 3 3 1 3 3 3 3 3 3 1 3 1 3 1 3 3 3 3 1 1 3
## [1531] 3 1 3 3 1 3 3 3 3 1 3 1 3 3 1 1 1 3 3 3 1 3 3 3 1 3 1 1 3 3 3 3 1 3
## [1565] 1 1 3 3 3 3 3 3 3 1 1 3 3 3 3 3 1 1 1 3 3 1 3 1 3 3 3 1 3 3 3 1 3 3
## [1599] 3 3 3 3 1 3 1 3 1 1 3 3 3 3 3 3 1 3 3 1 3 3 3 3 1 3 3 3 3 3 3 3 1 3
## [1633] 3 1 3 3 3 3 3 3 3 3 3 3 3 1 3 3 1 3 3 3 3 1 3 3 1 3 3 3 3 1 3 1 3 3
## [1667] 3 3 3 3 3 1 1 3 1 3 1 3 1 3 1 3 3 3 3 3 1 3 3 3 3 1 3 3 1 3 3 3 3 3
## [1701] 3 1 1 3 3 3 3 3 1 3 1 3 1 1 3 3 3 1 3 3 3 3 3 3 3 3 1 1 3 3 1 3 3 3
## [1735] 3 3 3 3 1 1 1 3 3 3 1 3 1 3 3 1 1 1 3 1 3 1 3 1 1 3 1 3 3 1 3 3 3 1
## [1769] 3 1 3 3 3 3 3 3 1 3 3 1 1 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 1 3 3 1
## [1803] 3 3 3 3 3 3 1 3 3 1 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 1 3 1 3 3 1
## [1837] 3 1 1 3 1 3 1 3 1 3 3 1 3 1 3 3 3 3 3 1 3 1 3 1 1 3 3 3 3 1 1 1 3 3
## [1871] 3 1 3 3 3 3 3 3 1 3 3 1 1 3 3 1 1 3 3 3 3 3 3 3 1 1 1 3 3 3 1 1 3 3
## [1905] 1 3 3 1 3 3 3 3 3 1 1 1 3 3 1 3 3 3 1 3 1 3 3 1 3 3 1 3 1 1 3 3 1 3
## [1939] 3 3 3 1 3 3 3 3 3 3 3 3 3 2 3 3 3 1 1 3 3 1 3 3 3 3 3 3 1 3 3 1 3 3
## [1973] 3 1 3 3 1 3 3 3 1 3 1 1 1 3 3 3 1 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1
## [2007] 1 1 3 3 3 3 1 1 1 3 1 1 3 1 3 3 3 1 3 3 1 3 3 3 3 3 3 3 3 3 1 3 1 3
## [2041] 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 1 3 1 1 3 1 3 3 3 3 3 3 3 3 1 3
## [2075] 1 3 1 3 1 1 3 3 3 1 3 3 1 3 3 1 3 3 1 1 3 3 3 3 3 3 3 3 3 3 3 1 3 1
## [2109] 3 3 1 3 3 1 3 3 3 3 1 3 1 3 3 3 1 3 1 3 1 3 3 3 3 1 3 3 3 3 1 3 1 3
## [2143] 3 3 3 1 3 3 3 3 3 2 3 3 3 3 3 3 3 1 1 1 3 3 3 1 1 1 1 3 3 1 3 3 3 1
## [2177] 1 3 3 3 1 1 3 1 3 1 3 1 3 1 3 1 3 3 1 3 3 3 3 3 3 3 3 3 1 3 3 1 3 3
## [2211] 1 3 3 3 1 3 1 3 3 3 1 1 3 1 3 3 3 1 1 3 1 3 3 3 3 3 3 3 1 3 3 3 1 1
## [2245] 3 3 1 3 3 3 1 3 1 3 1 3 3 3 3 3 1 3 3 3 1 1 3 3 3 3 3 3 3 1 3 3 3 1
## [2279] 3 1 3 3 1 3 3 3 3 3 3 1 3 3 1 1 3 3 3 3 3 1 3 3 1 3 3 3 1 3 1 3 3 3
## [2313] 3 1 3 3 3 3 3 3 3 3 1 3 3 1 3 1 1 3 3 3 3 3 1 1 3 3 3 3 3 3 3 1 1 3
## [2347] 3 3 3 3 3 3 3 1 3 3 2 3 3 3 3 3 3 3 3 1 1 3 3 1 1 3 3 1 3 1 3 3 3 1
## [2381] 1 1 3 3 3 3 3 1 3 3 3 3 1 3 1 3 3 3 1 3 1 3 1 1 3 3 1 3 3 3 3 3 3 3
## [2415] 3 3 3 1 1 3 1 3 3 3 3 3 3 3 3 1 3 3 1 3 1 2 3 3 3 1 3 3 3 1 3 3 1 3
## [2449] 1 3 3 3 3 3 3 3 3 1 1 3 3 1 3 1 3 3 3 1 3 3 1 1 3 3 3 1 3 1 3 3 3 3
## [2483] 3 3 3 3 3 3 1 3 3 1 1 3 3 1 3 3 1 1 3 1 1 3 3 1 1 1 3 3 1 3 1 1 1 3
## [2517] 2 3 3 1 1 1 3 3 1 3 3 1 3 3 3 1 1 3 1 3 3 1 1 3 3 3 3 1 1 1 3 1 1 3
## [2551] 3 3 3 3 3 3 1 3 1 3 3 3 1 3 3 1 1 3 1 3 3 2 3 3 3 1 3 3 1 1 3 3 1 3
## [2585] 1 1 3 1 3 3 3 3 1 3 1 1 3 3 3 3 3 1 1 1 3 1 1 3 3 1 3 3 1 3 3 1 1 1
## [2619] 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 1 3 3 1 3 3 3 1 3 3 3 3 3 3 1 3 1 3 1
## [2653] 3 3 3 1 1 1 3 3 1 3 3 3 3 3 1 3 3 3 3 1 1 3 1 3 3 3 3 1 1 3 1 3 3 3
## [2687] 3 1 3 3 3 3 3 3 3 3 1 3 3 3 1 3 3 1 3 3 3 3 1 3 3 3 1 1 3 1 1 3 1 3
## [2721] 1 3 3 3 3 1 1 3 3 3 3 3 1 3 1 3 3 1 1 3 3 1 3 3 3 3 3 3 1 3 3 1 3 3
## [2755] 3 1 3 3 3 3 3 3 3 3 3 3 1 1 1 3 3 3 3 3 1 3 3 1 3 3 1 1 3 3 3 3 3 1
## [2789] 1 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 1 1 3 3 3 3 3 1 3 1 1 3 1 3 3 3 1 3
## [2823] 3 3 1 3 1 3 3 1 3 3 1 3 3 1 3 3 1 3 3 3 1 3 3 3 1 3 3 3 3 3 3 1 3 3
## [2857] 3 1 3 3 3 1 1 1 1 3 3 3 3 3 1 3 1 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3
## [2891] 3 3 1 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 1 3 1 3 1 1 3 3 3 3 1 3 3 3
## [2925] 3 3 3 3 3 3 3 3 3 1 3 1 3 3 3 3 3 3 3 1 3 3 3 3 3 1 3 1 3 3 1 3 3 3
## [2959] 1 3 3 3 3 3 1 1 3 3 3 1 3 1 3 3 1 3 1 3 3 3 3 3 1 1 1 3 1 3 3 3 3 1
## [2993] 3 3 1 1 3 3 3 3
##
## Within cluster sum of squares by cluster:
## [1] 2.680e+10 8.563e+09 1.327e+10
## (between_SS / total_SS = 61.0 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"summary(market2)## age Estimated_income recent_spends family_size
## Min. :21.0 Min. : 0 Min. : 0 Min. :1.00
## 1st Qu.:41.0 1st Qu.: 1500 1st Qu.: 0 1st Qu.:1.00
## Median :52.0 Median : 4200 Median : 437 Median :1.00
## Mean :52.3 Mean : 5177 Mean : 1499 Mean :1.73
## 3rd Qu.:63.0 3rd Qu.: 7370 3rd Qu.: 2005 3rd Qu.:2.00
## Max. :97.0 Max. :142000 Max. :38786 Max. :8.00
## Avg_visits_permonth
## Min. : 1.00
## 1st Qu.: 3.00
## Median : 6.00
## Mean : 5.54
## 3rd Qu.: 8.00
## Max. :10.00Since we are not able to arrive with proper cluster, we’ll do some changes in the data.
market3 <- market2 %>%
filter(recent_spends != 0) #removing observations who didn't spend at all in the supermarket
market3## Source: local data frame [2,215 x 5]
##
## age Estimated_income recent_spends family_size Avg_visits_permonth
## 1 30 3300 7.716e+02 1 4
## 2 46 12454 1.289e+02 3 3
## 3 38 3000 7.697e+01 3 3
## 4 39 2500 2.500e+03 1 1
## 5 24 750 7.500e+02 1 5
## 6 68 1 3.689e-01 1 3
## 7 38 13000 1.084e+04 3 9
## 8 29 2231 2.213e+03 1 6
## 9 46 3326 3.256e+01 3 2
## 10 27 764 5.195e+00 1 8
## .. ... ... ... ... ...We will also take the log transformation of variables ‘Estimated_income’ and ‘recent_spends’ and add the log transformated variables for cluster analysis instead of original variables.
market4 <- market3 %>%
mutate(est_income = log(Estimated_income), rec_spends = log(recent_spends))
market5 <- market4 %>%
select(age, family_size:rec_spends)
market5## Source: local data frame [2,215 x 5]
##
## age family_size Avg_visits_permonth est_income rec_spends
## 1 30 1 4 8.102 6.6484
## 2 46 3 3 9.430 4.8592
## 3 38 3 3 8.006 4.3434
## 4 39 1 1 7.824 7.8240
## 5 24 1 5 6.620 6.6201
## 6 68 1 3 0.000 -0.9973
## 7 38 3 9 9.473 9.2912
## 8 29 1 6 7.710 7.7022
## 9 46 3 2 8.110 3.4830
## 10 27 1 8 6.639 1.6476
## .. ... ... ... ... ...Looking for the best cluster
market5_cluster1 <- kmeans(market5, 6)
market5_cluster1## K-means clustering with 6 clusters of sizes 446, 498, 143, 258, 362, 508
##
## Cluster means:
## age family_size Avg_visits_permonth est_income rec_spends
## 1 59.72 1.621 5.527 8.654 6.488
## 2 41.66 2.412 5.542 8.477 6.918
## 3 80.10 1.182 5.867 8.423 5.284
## 4 69.07 1.256 5.333 8.506 6.024
## 5 31.05 1.688 5.528 8.148 6.760
## 6 51.16 2.059 5.443 8.667 6.835
##
## Clustering vector:
## [1] 5 2 2 2 5 4 2 5 2 5 5 6 5 4 1 2 1 6 2 2 5 1 3 1 1 1 3 1 6 1 2 4 1 5
## [35] 6 6 1 4 2 2 2 5 1 2 6 2 2 2 3 1 6 5 6 5 5 1 4 4 1 6 4 4 6 5 5 6 5 2
## [69] 1 6 5 2 6 4 1 6 6 6 1 1 3 2 6 1 5 4 1 1 6 5 3 1 6 6 2 2 2 6 5 4 5 5
## [103] 2 3 2 5 1 4 4 6 2 6 5 2 5 1 1 2 2 5 4 3 6 1 1 3 6 6 6 2 1 5 3 6 1 2
## [137] 6 6 5 1 1 2 4 4 5 6 6 1 5 1 6 3 2 6 1 6 5 2 3 4 6 2 1 1 5 6 6 1 4 6
## [171] 5 6 2 3 5 5 4 4 1 5 4 3 6 6 1 3 2 1 5 1 2 6 5 5 6 2 2 5 5 5 2 5 2 3
## [205] 4 6 6 5 6 6 1 1 6 2 2 1 2 5 5 2 2 2 6 5 2 2 5 1 3 6 6 1 4 1 6 5 5 4
## [239] 2 4 5 6 6 1 6 5 6 2 3 5 1 6 3 6 2 1 1 3 1 2 4 4 6 1 4 1 5 4 2 1 6 1
## [273] 2 6 3 6 2 4 5 1 6 1 5 5 2 5 5 5 2 2 2 1 6 3 1 2 4 6 6 1 5 2 1 5 2 6
## [307] 5 3 1 1 1 2 1 2 6 2 2 2 1 6 5 6 6 6 2 2 5 4 2 6 6 2 5 5 6 6 2 2 4 4
## [341] 5 2 2 3 5 4 3 6 2 1 5 1 2 1 5 3 2 2 4 2 6 1 4 6 6 1 2 5 2 5 1 1 4 6
## [375] 6 1 2 2 6 2 5 2 2 3 1 5 4 6 6 1 2 6 5 5 4 1 6 2 1 1 5 1 1 3 5 2 1 6
## [409] 1 1 2 6 2 3 2 5 5 5 6 2 4 6 6 5 4 5 6 1 6 4 2 5 6 5 1 6 6 6 4 5 5 6
## [443] 1 3 1 2 2 1 1 5 1 5 6 1 1 6 3 2 2 1 5 6 6 6 1 6 5 6 6 6 6 5 4 1 2 6
## [477] 1 6 4 6 1 1 2 5 6 1 5 2 6 6 5 1 4 4 3 6 6 4 1 1 3 5 6 1 4 5 3 6 4 3
## [511] 6 4 6 4 2 6 5 2 3 1 2 6 6 1 6 6 5 5 5 6 2 6 6 3 6 6 6 1 4 2 4 6 2 6
## [545] 2 6 1 2 2 2 2 1 4 1 3 2 2 4 1 2 5 6 5 5 3 2 3 5 6 2 2 1 1 6 1 2 5 6
## [579] 6 4 5 2 4 1 1 5 2 2 2 4 6 1 4 1 2 6 5 5 3 1 6 4 2 2 1 1 6 2 3 1 2 1
## [613] 3 2 2 2 5 5 2 6 2 1 2 2 5 1 5 2 2 5 6 4 6 5 1 2 6 1 5 2 4 2 1 2 1 4
## [647] 1 1 5 1 4 6 4 6 3 5 6 2 5 6 3 6 4 6 5 6 2 2 1 5 1 3 4 6 5 2 1 1 5 1
## [681] 2 6 4 1 6 2 4 6 4 3 6 6 6 4 1 1 1 6 5 5 6 5 6 3 6 5 6 5 1 6 6 6 1 1
## [715] 2 1 6 1 6 2 2 5 2 6 5 1 3 6 6 4 2 2 1 3 6 2 6 1 5 6 1 1 3 4 3 5 1 5
## [749] 6 2 4 2 3 5 5 6 5 1 1 6 5 2 5 4 6 4 5 2 5 6 3 5 4 6 5 1 5 5 3 5 3 5
## [783] 1 4 4 2 6 4 1 6 5 6 2 6 1 2 2 4 2 6 5 6 3 5 4 5 2 1 6 1 2 5 5 1 6 6
## [817] 6 6 1 2 1 1 1 5 1 6 3 6 1 4 6 6 4 2 6 6 5 3 2 5 1 4 2 5 5 6 5 1 2 1
## [851] 3 6 4 4 1 1 5 6 1 1 2 5 6 3 4 5 6 5 4 2 4 1 4 6 6 4 2 5 1 5 6 4 5 6
## [885] 5 5 6 2 4 6 6 6 2 5 6 5 2 6 6 5 4 6 3 2 4 2 6 1 6 1 3 6 1 2 4 6 5 5
## [919] 6 1 2 6 6 1 3 6 6 3 6 4 1 6 2 1 5 2 2 2 5 2 5 1 1 1 1 2 5 3 3 5 6 5
## [953] 2 1 2 4 4 1 1 5 2 1 4 6 6 2 6 1 2 4 2 6 2 3 6 1 4 1 4 6 2 2 4 4 6 5
## [987] 4 5 2 4 1 2 3 2 6 6 1 1 2 6 2 2 6 1 2 1 2 5 2 6 6 4 3 2 6 4 5 5 5 2
## [1021] 5 6 1 4 6 4 1 4 6 2 4 5 6 6 2 4 2 6 3 6 2 6 5 2 5 2 6 4 1 5 4 1 5 5
## [1055] 2 6 2 6 6 4 6 2 5 3 1 1 2 1 3 1 1 5 6 1 2 2 4 2 4 6 2 6 1 1 1 1 2 5
## [1089] 2 6 4 6 2 2 6 4 4 2 2 6 1 2 6 1 4 2 1 6 4 3 4 1 1 6 5 1 5 4 2 3 4 4
## [1123] 3 3 1 2 2 1 1 2 2 6 6 4 2 1 4 6 2 6 5 5 1 3 3 6 6 6 2 3 4 1 2 6 1 6
## [1157] 5 2 2 1 2 2 6 6 4 2 2 6 6 6 2 1 6 2 2 3 3 2 2 4 1 4 1 5 4 1 2 5 6 4
## [1191] 5 4 6 4 2 5 2 1 6 5 2 2 1 2 2 6 4 5 5 6 1 5 2 1 1 2 4 5 6 5 2 5 6 5
## [1225] 4 2 6 2 2 4 6 6 2 5 2 5 1 1 1 6 5 1 2 6 5 2 5 1 5 3 1 1 1 2 6 2 1 5
## [1259] 5 6 5 6 1 1 6 6 1 1 1 5 6 6 5 5 6 6 4 6 6 2 4 1 1 5 6 1 1 3 4 1 2 1
## [1293] 1 4 1 2 2 2 6 6 1 1 6 5 2 6 2 2 4 5 6 2 3 6 2 5 2 1 3 6 6 2 5 5 1 1
## [1327] 1 5 2 4 4 1 4 2 1 5 1 5 5 2 2 6 5 3 2 2 4 6 6 5 1 2 6 2 5 1 6 1 3 2
## [1361] 1 1 2 4 6 4 2 6 1 3 4 2 6 6 2 6 2 5 6 5 2 6 1 1 4 2 2 6 2 3 4 2 2 2
## [1395] 2 5 6 6 4 4 6 2 4 6 2 4 1 6 6 1 6 3 6 2 5 1 6 6 2 2 5 2 1 1 2 2 2 6
## [1429] 1 6 4 6 4 6 5 6 6 5 6 5 4 2 1 5 5 4 4 1 2 1 6 4 6 6 1 5 1 2 6 6 2 2
## [1463] 2 6 2 1 6 5 1 6 5 5 2 1 6 2 5 4 3 5 1 6 2 6 1 6 6 3 4 4 4 6 2 3 5 5
## [1497] 6 6 4 6 2 2 6 3 5 4 2 1 3 1 1 2 3 6 2 2 5 1 6 2 1 1 5 4 2 5 1 6 2 5
## [1531] 2 1 6 2 2 3 4 2 1 5 1 2 5 2 1 6 1 6 3 2 3 6 3 5 5 2 1 1 5 4 2 5 5 2
## [1565] 2 5 1 2 2 3 2 5 1 5 1 1 4 4 6 6 2 4 1 4 5 5 6 6 1 6 6 1 6 6 2 1 1 2
## [1599] 5 1 6 4 5 2 2 1 5 1 1 5 6 1 1 1 3 5 2 1 2 2 6 2 2 4 4 6 4 4 1 4 6 1
## [1633] 1 6 2 3 1 2 2 6 2 6 5 6 5 4 1 2 3 6 2 2 2 5 1 2 3 2 5 6 1 6 5 6 4 5
## [1667] 4 6 6 6 1 5 2 6 1 1 2 1 3 2 5 3 6 1 3 2 1 3 1 1 3 1 2 6 5 6 1 1 5 1
## [1701] 2 5 1 1 1 5 2 6 6 2 3 4 4 6 4 2 5 1 2 2 6 6 2 5 1 5 6 6 4 2 2 6 1 1
## [1735] 6 2 3 1 4 2 4 6 1 5 2 2 4 6 1 3 1 5 1 2 2 6 1 1 6 2 5 4 2 2 1 2 1 2
## [1769] 1 1 4 1 2 1 6 5 1 2 6 2 4 5 4 6 6 6 3 5 1 4 5 3 5 5 1 2 2 3 4 4 2 3
## [1803] 6 2 3 1 1 6 1 2 4 6 6 1 2 1 2 5 6 2 2 6 1 4 3 1 1 1 5 6 4 5 3 6 2 6
## [1837] 2 2 6 3 6 2 4 2 6 6 2 2 6 5 3 2 1 1 6 5 3 2 6 4 1 6 4 2 6 5 1 1 1 4
## [1871] 5 2 5 1 4 5 5 6 6 1 6 5 4 1 6 1 5 5 4 4 6 1 2 5 4 5 3 1 4 1 5 6 1 1
## [1905] 6 6 4 2 2 6 4 6 2 3 4 6 6 1 2 5 4 2 6 2 2 6 2 2 6 3 6 6 3 2 1 2 1 2
## [1939] 1 1 4 2 4 3 2 6 6 2 6 5 2 5 6 5 4 6 1 2 2 1 5 1 1 4 5 6 6 1 5 6 2 1
## [1973] 1 6 5 1 5 1 2 4 6 5 6 4 3 2 2 4 3 6 2 6 4 3 2 2 2 1 4 5 3 2 1 6 2 2
## [2007] 6 6 3 1 1 6 2 2 2 2 2 2 2 6 1 1 5 4 1 6 5 6 1 3 1 2 2 2 2 4 4 2 2 4
## [2041] 1 4 5 2 6 5 5 4 1 2 5 5 2 5 2 6 4 3 1 6 5 4 4 3 2 5 2 6 6 2 2 2 1 1
## [2075] 1 6 1 5 5 4 3 2 2 1 4 1 4 2 1 2 2 4 5 2 6 2 5 2 5 1 1 1 6 4 6 5 3 1
## [2109] 1 2 6 6 2 5 4 1 6 1 6 6 2 1 1 1 2 1 5 2 5 6 4 6 2 3 2 5 4 6 6 2 4 2
## [2143] 1 3 3 2 2 1 5 6 1 6 2 2 2 4 5 4 5 5 4 5 2 2 1 3 1 1 4 2 5 6 6 5 6 4
## [2177] 4 2 6 2 2 6 6 6 2 6 6 5 6 1 5 1 1 5 1 1 4 3 6 2 6 4 6 6 6 6 2 1 2 5
## [2211] 1 4 5 5 6
##
## Within cluster sum of squares by cluster:
## [1] 9150 11376 4639 5290 9705 10334
## (between_SS / total_SS = 89.4 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"market5_cluster2 <- kmeans(market5, 4)
market5_cluster2## K-means clustering with 4 clusters of sizes 401, 765, 680, 369
##
## Cluster means:
## age family_size Avg_visits_permonth est_income rec_spends
## 1 31.63 1.748 5.586 8.169 6.745
## 2 57.47 1.722 5.584 8.678 6.642
## 3 44.18 2.359 5.422 8.518 6.875
## 4 73.70 1.225 5.439 8.472 5.710
##
## Clustering vector:
## [1] 1 3 3 3 1 4 3 1 3 1 1 3 1 4 2 1 2 3 3 3 1 2 4 2 2 2 4 2 2 2 1 4 2 1
## [35] 3 2 2 4 3 3 1 1 2 3 3 3 3 3 4 2 2 1 3 1 1 2 4 4 2 3 4 4 3 1 1 3 1 3
## [69] 2 2 1 3 3 4 2 3 2 2 2 2 4 3 2 2 1 4 2 2 3 1 4 2 2 2 3 3 3 2 1 4 1 1
## [103] 3 4 3 1 2 4 4 2 3 3 1 3 1 2 2 3 3 1 4 4 2 2 2 4 2 3 2 3 2 1 4 2 2 1
## [137] 2 2 1 2 2 3 4 2 1 3 2 2 1 2 3 4 3 2 2 3 1 3 4 4 3 3 2 2 1 3 2 2 4 3
## [171] 1 2 3 4 1 1 4 4 2 1 4 4 2 3 2 4 3 2 1 2 3 2 1 1 2 1 3 1 1 1 3 1 3 4
## [205] 2 3 2 1 2 3 2 2 3 3 3 2 3 1 1 1 3 3 3 1 3 3 1 2 4 2 3 2 4 2 2 1 1 4
## [239] 3 4 1 3 2 2 2 1 2 3 4 1 2 3 4 3 3 2 2 4 2 3 4 4 2 2 4 2 1 4 3 2 2 2
## [273] 3 2 4 2 3 4 1 2 2 2 1 1 3 1 1 1 3 3 3 2 3 4 2 3 4 2 3 2 1 3 2 1 3 2
## [307] 1 4 2 2 2 3 2 3 3 3 3 3 2 3 1 2 2 2 3 3 1 2 3 3 2 3 1 1 2 2 1 3 4 4
## [341] 1 3 3 4 1 2 4 3 3 2 1 2 3 2 1 4 3 1 4 3 3 2 4 2 2 2 3 1 3 1 2 2 4 3
## [375] 2 2 1 3 3 3 1 3 3 4 2 1 4 2 2 2 3 3 1 1 4 2 2 3 2 2 1 2 2 4 1 3 2 2
## [409] 2 2 3 3 3 4 3 1 1 1 3 3 4 3 3 1 4 1 3 2 2 4 3 1 2 1 2 2 2 3 2 1 1 2
## [443] 2 4 2 3 3 2 2 1 2 1 2 2 2 2 4 3 3 2 1 3 2 2 2 3 1 2 2 2 2 1 4 2 3 2
## [477] 2 2 4 2 2 2 3 1 2 2 1 3 2 3 1 2 4 4 4 3 3 2 2 2 4 1 2 2 4 1 4 3 4 4
## [511] 3 4 3 4 3 2 1 3 4 2 3 3 2 2 3 2 1 1 1 3 3 2 3 4 2 2 2 2 4 3 4 2 3 3
## [545] 3 2 2 3 3 3 3 2 4 2 4 3 3 4 2 3 1 3 1 1 4 3 4 1 3 3 3 2 2 2 2 3 1 3
## [579] 2 4 1 3 4 2 2 1 3 3 1 2 3 2 4 2 3 3 1 1 4 2 3 4 3 3 2 2 3 3 4 2 1 2
## [613] 4 3 3 3 1 1 3 2 3 2 3 3 1 2 1 1 3 1 3 4 2 1 2 3 2 2 1 3 4 3 2 3 2 4
## [647] 2 2 1 2 4 3 4 2 4 1 3 3 1 2 4 2 4 2 1 2 3 3 2 1 2 4 4 2 1 3 2 2 1 2
## [681] 3 3 4 2 3 3 4 3 4 4 2 2 2 4 2 2 2 3 1 1 3 1 3 4 2 1 2 1 2 3 2 2 2 2
## [715] 3 2 3 2 2 3 3 1 3 3 1 2 4 3 3 4 3 3 2 4 2 1 2 2 1 2 2 2 4 4 4 1 2 1
## [749] 2 3 4 3 4 1 1 3 1 2 2 3 1 3 1 4 2 2 1 3 1 3 4 1 4 3 1 2 1 1 4 1 4 1
## [783] 2 4 4 3 2 4 2 2 1 2 3 2 2 1 3 2 3 2 1 3 4 1 4 1 3 2 3 2 3 1 1 2 3 3
## [817] 2 3 2 3 2 2 2 1 2 3 4 3 2 4 2 2 4 3 3 2 1 4 3 1 2 4 1 1 1 2 1 2 3 2
## [851] 4 3 4 2 2 2 1 3 2 2 3 1 2 4 4 1 2 1 4 3 4 2 2 2 2 4 3 1 2 1 2 2 1 3
## [885] 1 1 2 3 4 3 3 2 3 1 3 1 3 2 2 1 4 2 4 3 4 3 3 2 2 2 4 2 2 3 4 3 1 1
## [919] 2 2 3 3 2 2 4 2 2 4 2 4 2 3 3 2 1 3 3 3 1 3 1 2 2 2 2 1 1 4 4 1 3 1
## [953] 3 2 3 4 4 2 2 1 3 2 4 2 2 3 3 2 3 4 3 2 3 4 3 2 2 2 4 3 3 3 2 4 2 1
## [987] 4 1 3 4 2 3 4 1 3 2 2 2 3 2 3 3 2 2 3 2 1 1 3 2 3 4 4 3 3 4 1 1 1 3
## [1021] 1 3 2 4 2 4 2 4 3 3 4 1 2 2 3 2 3 3 4 2 3 3 1 3 1 1 2 4 2 1 4 2 1 1
## [1055] 3 2 3 2 2 4 2 3 1 4 2 2 3 2 4 2 2 1 2 2 3 3 4 3 2 3 3 2 2 2 2 2 3 1
## [1089] 3 3 2 2 3 3 3 4 2 3 3 2 2 3 2 2 4 3 2 2 2 4 4 2 2 3 1 2 1 4 3 4 2 4
## [1123] 4 4 2 3 3 2 2 3 3 2 3 4 3 2 4 2 3 3 1 1 2 4 4 2 2 2 3 4 4 2 3 2 2 3
## [1157] 1 3 3 2 3 3 3 3 4 3 3 3 2 2 3 2 3 3 3 4 4 3 3 4 2 4 2 1 4 2 1 1 2 4
## [1191] 1 4 2 4 3 1 1 2 3 1 3 3 2 3 3 2 4 1 1 3 2 1 3 2 2 3 4 1 2 1 3 1 3 1
## [1225] 4 3 2 3 3 4 2 3 3 1 3 1 2 2 2 3 1 2 3 2 1 3 1 2 1 4 2 2 2 3 2 3 2 1
## [1259] 1 3 1 2 2 2 2 2 2 2 2 1 3 3 1 1 2 3 4 2 2 3 4 2 2 1 2 2 2 4 4 2 3 2
## [1293] 2 4 2 3 3 3 3 3 2 2 3 1 3 3 3 1 4 1 2 3 4 3 3 1 3 2 4 2 3 1 1 1 2 2
## [1327] 2 1 3 4 4 2 4 3 2 1 2 1 1 3 3 2 1 4 3 3 2 2 2 1 2 3 2 3 1 2 3 2 4 3
## [1361] 2 2 3 4 2 4 3 3 2 4 4 3 2 2 3 2 3 1 3 1 3 2 2 2 4 3 3 3 3 4 4 3 1 3
## [1395] 3 1 3 3 4 4 2 3 4 2 3 4 2 3 3 2 2 4 2 3 1 2 2 2 3 3 1 3 2 2 3 3 3 3
## [1429] 2 2 4 2 4 2 1 3 2 1 2 1 4 3 2 1 1 4 4 2 3 2 2 4 2 3 2 1 2 3 2 2 3 1
## [1463] 3 3 3 2 3 1 2 2 1 1 3 2 2 3 1 4 4 1 2 2 3 3 2 2 2 4 4 4 4 3 3 4 1 1
## [1497] 2 2 4 3 3 3 2 4 1 4 3 2 4 2 2 1 4 3 1 3 1 2 3 1 2 2 1 2 3 1 2 3 3 1
## [1531] 3 2 3 3 3 4 4 3 2 1 2 3 1 3 2 3 2 3 4 3 4 2 4 1 1 3 2 2 1 4 3 1 1 3
## [1565] 3 1 2 3 3 4 3 1 2 1 2 2 4 4 2 2 3 4 2 2 1 1 3 2 2 3 3 2 3 2 3 2 2 3
## [1599] 1 2 3 2 1 3 3 2 1 2 2 1 2 2 2 2 4 1 3 2 3 3 2 3 3 4 4 2 4 4 2 4 2 2
## [1633] 2 3 3 4 2 1 3 2 3 3 1 3 1 4 2 3 4 2 3 3 3 1 2 3 4 3 1 2 2 2 1 2 4 1
## [1667] 4 2 3 2 2 1 3 3 2 2 3 2 4 3 1 4 2 2 4 3 2 4 2 2 4 2 3 3 1 2 2 2 1 2
## [1701] 3 1 2 2 2 1 3 2 2 3 4 4 4 2 4 3 1 2 3 3 3 2 3 1 2 1 2 2 4 3 3 3 2 2
## [1735] 2 3 4 2 2 3 4 2 2 1 3 1 4 3 2 4 2 1 2 3 3 2 2 2 2 1 1 2 3 3 2 3 2 3
## [1769] 2 2 4 2 3 2 3 1 2 3 2 3 4 1 4 2 2 2 4 1 2 4 1 4 1 1 2 3 3 4 4 4 3 4
## [1803] 2 3 4 2 2 2 2 3 4 3 3 2 3 2 3 1 2 3 3 3 2 4 4 2 2 2 1 2 4 1 4 2 3 3
## [1837] 3 3 3 4 2 3 4 3 2 3 3 3 3 1 4 3 2 2 3 1 4 3 3 4 2 2 4 3 2 1 2 2 2 4
## [1871] 1 3 1 2 4 1 1 2 3 2 3 1 4 2 2 2 1 1 4 4 3 2 3 1 4 1 4 2 4 2 1 3 2 2
## [1905] 3 3 4 3 1 2 4 3 3 4 2 2 3 2 1 1 4 1 2 3 3 2 3 3 3 4 2 3 4 3 2 3 2 3
## [1939] 2 2 4 3 4 4 3 3 2 3 3 1 3 1 2 1 4 3 2 3 3 2 1 2 2 2 1 2 2 2 1 3 3 2
## [1973] 2 2 1 2 1 2 3 4 3 1 3 4 4 3 3 4 4 2 3 3 2 4 3 3 3 2 4 1 4 3 2 3 3 3
## [2007] 3 3 4 2 2 3 3 3 1 3 3 3 3 2 2 2 1 4 2 2 1 2 2 4 2 3 3 3 3 4 4 3 3 4
## [2041] 2 4 1 3 2 1 1 4 2 3 1 1 1 1 3 2 4 4 2 2 1 4 2 4 3 1 3 3 2 3 3 3 2 2
## [2075] 2 2 2 1 1 2 4 3 3 2 2 2 4 3 2 3 3 4 1 3 2 3 1 3 1 2 2 2 3 4 2 1 4 2
## [2109] 2 1 3 2 3 1 4 2 2 2 3 2 3 2 2 2 3 2 1 3 1 2 4 2 1 4 3 1 4 3 2 3 4 1
## [2143] 2 4 4 3 3 2 1 2 2 3 3 3 3 4 1 4 1 1 4 1 3 3 2 4 2 2 4 3 1 2 2 1 3 4
## [2177] 4 3 3 3 3 2 3 2 3 3 3 1 3 2 1 2 2 1 2 2 4 4 3 3 3 4 3 2 2 3 3 2 3 1
## [2211] 2 4 1 1 3
##
## Within cluster sum of squares by cluster:
## [1] 11671 23445 19572 18548
## (between_SS / total_SS = 84.7 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"market5_cluster3 <- kmeans(market5, 3)
market5_cluster3## K-means clustering with 3 clusters of sizes 754, 490, 971
##
## Cluster means:
## age family_size Avg_visits_permonth est_income rec_spends
## 1 36.03 2.080 5.544 8.324 6.845
## 2 71.27 1.259 5.537 8.500 5.842
## 3 53.35 1.945 5.472 8.642 6.740
##
## Clustering vector:
## [1] 1 3 1 1 1 2 1 1 3 1 1 3 1 2 2 1 3 3 1 1 1 3 2 3 3 2 2 3 3 2 1 2 2 1
## [35] 3 3 2 2 3 1 1 1 2 1 3 1 1 1 2 3 3 1 3 1 1 3 2 2 3 3 2 2 3 1 1 3 1 1
## [69] 2 3 1 1 3 2 3 3 3 3 2 2 2 1 3 3 1 2 3 3 3 1 2 3 3 3 1 1 1 3 1 2 1 1
## [103] 1 2 3 1 2 2 2 3 1 3 1 1 1 3 2 1 1 1 2 2 3 2 3 2 3 3 3 1 3 1 2 3 2 1
## [137] 3 3 1 3 3 1 2 2 1 3 3 2 1 3 3 2 1 3 3 3 1 1 2 2 3 1 3 3 1 3 3 3 2 3
## [171] 1 3 1 2 1 1 2 2 3 1 2 2 3 3 2 2 1 3 1 3 1 3 1 1 3 1 1 1 1 1 1 1 1 2
## [205] 2 3 3 1 3 3 3 2 3 3 1 3 1 1 1 1 1 3 3 1 1 1 1 3 2 3 3 2 2 3 3 1 1 2
## [239] 1 2 1 3 3 3 3 1 3 3 2 1 3 3 2 3 1 3 3 2 3 3 2 2 3 3 2 3 1 2 1 2 3 3
## [273] 3 3 2 3 3 2 1 2 3 3 1 1 1 1 1 1 3 1 1 3 3 2 2 1 2 3 3 3 1 1 3 1 1 3
## [307] 1 2 3 2 3 1 3 1 3 1 1 1 3 3 1 3 3 3 1 1 1 2 1 3 3 3 1 1 3 3 1 1 2 2
## [341] 1 1 1 2 1 2 2 3 1 3 1 3 1 2 1 2 1 1 2 1 3 3 2 3 3 3 3 1 1 1 3 3 2 3
## [375] 3 3 1 1 3 1 1 1 1 2 3 1 2 3 3 3 1 3 1 1 2 3 3 1 2 3 1 3 3 2 1 1 3 3
## [409] 3 3 1 3 1 2 1 1 1 1 3 1 2 3 3 1 2 1 3 2 3 2 3 1 3 1 3 3 3 3 2 1 1 3
## [443] 2 2 2 1 1 3 2 1 3 1 3 3 3 3 2 1 1 3 1 3 3 3 2 3 1 3 3 3 3 1 2 3 3 3
## [477] 3 3 2 3 3 3 1 1 3 3 1 1 3 3 1 3 2 2 2 3 3 2 2 2 2 1 3 3 2 1 2 3 2 2
## [511] 3 2 3 2 3 3 1 1 2 3 1 3 3 3 3 3 1 1 1 3 3 3 3 2 3 3 3 3 2 1 2 3 1 3
## [545] 3 3 3 1 1 3 1 3 2 2 2 1 1 2 3 1 1 3 1 1 2 3 2 1 3 1 3 3 3 3 3 1 1 3
## [579] 3 2 1 1 2 3 3 1 1 1 1 2 3 3 2 3 1 3 1 1 2 2 3 2 1 3 3 3 3 1 2 3 1 3
## [613] 2 1 1 1 1 1 1 3 1 3 1 1 1 3 1 1 1 1 3 2 3 1 2 1 3 3 1 1 2 1 3 1 2 2
## [647] 3 3 1 3 2 3 2 3 2 1 3 3 1 3 2 3 2 3 1 3 3 3 3 1 3 2 2 3 1 1 3 3 1 3
## [681] 3 3 2 3 3 1 2 3 2 2 3 3 3 2 2 2 3 3 1 1 3 1 3 2 3 1 3 1 3 3 3 3 3 3
## [715] 3 3 3 3 3 1 3 1 1 3 1 3 2 3 3 2 1 1 3 2 3 1 3 3 1 3 3 2 2 2 2 1 3 1
## [749] 3 1 2 1 2 1 1 3 1 2 3 3 1 1 1 2 3 2 1 1 1 3 2 1 2 3 1 3 1 1 2 1 2 1
## [783] 3 2 2 3 3 2 3 3 1 3 1 3 3 1 1 2 3 3 1 3 2 1 2 1 1 2 3 3 1 1 1 3 3 3
## [817] 3 3 2 1 3 3 3 1 3 3 2 3 3 2 3 3 2 3 3 3 1 2 1 1 3 2 1 1 1 3 1 3 3 3
## [851] 2 3 2 2 3 3 1 3 3 2 3 1 3 2 2 1 3 1 2 1 2 3 2 3 3 2 1 1 3 1 3 2 1 3
## [885] 1 1 3 3 2 3 3 3 1 1 3 1 1 3 3 1 2 3 2 1 2 1 3 3 3 3 2 3 3 1 2 3 1 1
## [919] 3 3 3 3 3 3 2 3 3 2 3 2 3 3 3 3 1 1 3 1 1 1 1 3 3 3 2 1 1 2 2 1 3 1
## [953] 1 3 1 2 2 3 3 1 1 3 2 3 3 3 3 3 3 2 3 3 1 2 3 3 2 3 2 3 3 1 2 2 3 1
## [987] 2 1 1 2 3 1 2 1 3 3 3 3 1 3 1 1 3 3 1 2 1 1 1 3 3 2 2 3 3 2 1 1 1 1
## [1021] 1 3 3 2 3 2 2 2 3 1 2 1 3 3 1 2 1 3 2 3 1 3 1 3 1 1 3 2 3 1 2 3 1 1
## [1055] 1 3 1 3 3 2 3 1 1 2 3 3 1 3 2 3 3 1 3 3 1 1 2 1 2 3 3 3 2 3 2 3 3 1
## [1089] 1 3 2 3 3 1 3 2 2 1 1 3 3 1 3 3 2 1 3 3 2 2 2 3 3 3 1 3 1 2 1 2 2 2
## [1123] 2 2 3 3 3 3 3 1 3 3 3 2 1 3 2 3 1 3 1 1 3 2 2 3 3 3 1 2 2 3 1 3 3 3
## [1157] 1 1 1 3 1 1 3 3 2 1 1 3 3 3 1 3 3 3 3 2 2 1 1 2 3 2 3 1 2 3 1 1 3 2
## [1191] 1 2 3 2 1 1 1 3 3 1 3 1 3 1 1 3 2 1 1 3 2 1 3 2 3 3 2 1 3 1 1 1 3 1
## [1225] 2 1 3 3 1 2 3 3 1 1 1 1 3 3 3 3 1 3 3 3 1 1 1 2 1 2 2 3 3 1 3 3 3 1
## [1259] 1 3 1 3 3 2 3 3 3 2 3 1 3 3 1 1 3 3 2 3 3 3 2 3 3 1 3 2 3 2 2 3 1 3
## [1293] 3 2 3 3 1 1 3 3 3 3 3 1 1 3 1 1 2 1 3 1 2 3 3 1 3 2 2 3 3 1 1 1 3 3
## [1327] 3 1 3 2 2 3 2 1 2 1 3 1 1 3 1 3 1 2 3 1 2 3 3 1 2 1 3 3 1 2 3 3 2 1
## [1361] 2 3 1 2 3 2 3 3 3 2 2 3 3 3 1 3 3 1 3 1 1 3 3 3 2 1 1 3 3 2 2 1 1 1
## [1395] 1 1 3 3 2 2 3 1 2 3 3 2 3 3 3 3 3 2 3 3 1 2 3 3 1 1 1 3 2 3 1 1 3 3
## [1429] 3 3 2 3 2 3 1 3 3 1 3 1 2 1 2 1 1 2 2 3 3 3 3 2 3 3 3 1 2 1 3 3 3 1
## [1463] 1 3 1 3 3 1 3 3 1 1 1 2 3 1 1 2 2 1 3 3 3 3 2 3 3 2 2 2 2 3 1 2 1 1
## [1497] 3 3 2 3 1 1 3 2 1 2 1 2 2 3 3 1 2 3 1 1 1 3 3 1 3 2 1 2 1 1 3 3 1 1
## [1531] 1 3 3 1 1 2 2 1 3 1 2 1 1 3 3 3 3 3 2 1 2 3 2 1 1 1 3 2 1 2 3 1 1 1
## [1565] 1 1 3 1 1 2 1 1 3 1 2 3 2 2 3 3 1 2 3 2 1 1 3 3 2 3 3 3 3 3 1 3 3 1
## [1599] 1 3 3 2 1 1 1 3 1 3 3 1 3 2 3 3 2 1 1 2 1 1 3 1 1 2 2 3 2 2 3 2 3 3
## [1633] 3 3 1 2 3 1 1 3 1 3 1 3 1 2 3 1 2 3 1 1 1 1 3 1 2 1 1 3 3 3 1 3 2 1
## [1667] 2 3 3 3 3 1 3 3 3 3 1 3 2 1 1 2 3 3 2 1 3 2 3 3 2 3 1 3 1 3 3 3 1 2
## [1701] 1 1 3 3 3 1 3 3 3 1 2 2 2 3 2 1 1 3 1 1 3 3 1 1 2 1 3 3 2 1 1 3 3 2
## [1735] 3 1 2 3 2 1 2 3 2 1 1 1 2 3 3 2 3 1 3 1 1 3 3 2 3 1 1 2 1 1 3 1 3 1
## [1769] 3 3 2 3 3 3 3 1 3 1 3 1 2 1 2 3 3 3 2 1 2 2 1 2 1 1 3 1 1 2 2 2 1 2
## [1803] 3 3 2 3 3 3 3 1 2 3 3 3 3 2 1 1 3 1 1 3 3 2 2 3 3 3 1 3 2 1 2 3 1 3
## [1837] 1 1 3 2 3 1 2 3 3 3 1 1 3 1 2 1 3 3 3 1 2 1 3 2 3 3 2 1 3 1 2 3 3 2
## [1871] 1 1 1 3 2 1 1 3 3 3 3 1 2 3 3 2 1 1 2 2 3 3 1 1 2 1 2 2 2 2 1 3 3 3
## [1905] 3 3 2 1 1 3 2 3 1 2 2 3 3 3 1 1 2 1 3 1 3 3 3 1 3 2 3 3 2 1 3 1 3 1
## [1939] 3 3 2 1 2 2 1 3 3 3 3 1 1 1 3 1 2 3 3 3 1 3 1 3 3 2 1 3 3 3 1 3 3 3
## [1973] 3 3 1 3 1 3 1 2 3 1 3 2 2 1 1 2 2 3 1 3 2 2 1 1 1 3 2 1 2 3 3 3 1 3
## [2007] 3 3 2 3 3 3 1 1 1 1 3 3 1 3 3 3 1 2 3 3 1 3 3 2 3 1 3 1 1 2 2 1 3 2
## [2041] 2 2 1 3 3 1 1 2 3 1 1 1 1 1 1 3 2 2 3 3 1 2 2 2 1 1 1 3 3 1 1 3 3 3
## [2075] 3 3 3 1 1 2 2 1 1 2 2 3 2 3 3 3 3 2 1 1 3 1 1 1 1 3 3 3 3 2 3 1 2 3
## [2109] 3 1 3 3 3 1 2 3 3 2 3 3 1 3 3 3 1 2 1 1 1 3 2 3 1 2 1 1 2 3 3 1 2 1
## [2143] 3 2 2 1 1 3 1 3 3 3 1 1 3 2 1 2 1 1 2 1 3 3 3 2 2 3 2 1 1 3 3 1 3 2
## [2177] 2 1 3 1 3 3 3 3 1 3 3 1 3 3 1 3 3 1 3 2 2 2 3 3 3 2 3 3 3 3 1 3 1 1
## [2211] 3 2 1 1 3
##
## Within cluster sum of squares by cluster:
## [1] 34646 29244 38014
## (between_SS / total_SS = 78.7 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"We do the plotting as well
library("cluster")
library("fpc")We will take as 3 clusters and do the plot
clusplot(market5, market5_cluster3$cluster, color = TRUE, shade = TRUE, labels = 2, lines = 0)
plotcluster(market5, market5_cluster3$cluster)
We add the cluster variable to our table.
supermarket_cluster <- data.frame(market5, market5_cluster3$cluster)
supermarket_cluster <- tbl_df(supermarket_cluster)
supermarket_cluster## Source: local data frame [2,215 x 6]
##
## age family_size Avg_visits_permonth est_income rec_spends
## 1 30 1 4 8.102 6.6484
## 2 46 3 3 9.430 4.8592
## 3 38 3 3 8.006 4.3434
## 4 39 1 1 7.824 7.8240
## 5 24 1 5 6.620 6.6201
## 6 68 1 3 0.000 -0.9973
## 7 38 3 9 9.473 9.2912
## 8 29 1 6 7.710 7.7022
## 9 46 3 2 8.110 3.4830
## 10 27 1 8 6.639 1.6476
## .. ... ... ... ... ...
## Variables not shown: market5_cluster3.cluster (int)We check the mean values of all variables groupwise.
market5_cluster3$centers## age family_size Avg_visits_permonth est_income rec_spends
## 1 36.03 2.080 5.544 8.324 6.845
## 2 71.27 1.259 5.537 8.500 5.842
## 3 53.35 1.945 5.472 8.642 6.740