(97.6% of Variance is accounted for)
set.seed(188)
z <- kmeans(a[,-1],30)
final_withClusters <- cbind(a, clusternum = z$cluster)
z
## K-means clustering with 30 clusters of sizes 3, 6, 6, 7, 4, 7, 5, 7, 5, 6, 10, 7, 8, 5, 12, 10, 6, 3, 8, 4, 3, 12, 10, 6, 6, 5, 7, 3, 4, 3
##
## Cluster means:
## Market_Potential Total_Pop Per_Capita_Income
## 1 99.33333 82640.667 52083.00
## 2 101.16667 92959.667 25556.33
## 3 105.66667 98042.333 18284.00
## 4 85.42857 15739.143 85310.71
## 5 103.00000 58211.750 48083.75
## 6 104.57143 40161.286 16704.43
## 7 94.80000 61972.000 31562.60
## 8 104.28571 69453.143 19557.86
## 9 94.40000 73377.800 70627.00
## 10 88.83333 41743.667 83505.67
## 11 106.10000 112042.500 22255.60
## 12 95.85714 111038.143 48175.71
## 13 107.75000 82262.875 20234.88
## 14 94.00000 21839.400 36242.80
## 15 100.25000 14830.083 24343.92
## 16 93.00000 37065.500 45546.00
## 17 84.66667 6545.833 71528.50
## 18 95.33333 61960.667 13214.67
## 19 102.50000 159558.500 22220.25
## 20 101.75000 68287.000 40404.00
## 21 97.33333 54175.000 19695.67
## 22 98.83333 23190.500 16374.17
## 23 104.30000 7302.500 41404.00
## 24 95.00000 20831.333 59343.67
## 25 103.66667 84802.000 33715.00
## 26 85.00000 9954.400 108455.00
## 27 114.85714 3535.000 22649.57
## 28 87.33333 47593.333 31639.00
## 29 88.75000 49801.500 40204.00
## 30 96.33333 97108.667 66463.67
##
## Clustering vector:
## [1] 24 25 5 15 23 21 13 6 6 13 10 17 12 4 2 29 16 15 3 28 22 16 5
## [24] 11 14 11 24 7 12 2 29 15 2 4 26 18 27 11 27 11 13 26 13 10 4 2
## [47] 29 22 12 9 19 20 17 6 30 11 29 19 19 19 3 20 12 6 11 25 9 20 11
## [70] 19 19 2 7 13 25 25 10 16 3 30 9 9 11 5 13 19 12 25 8 2 6 14
## [93] 16 1 3 19 13 8 4 10 22 16 8 7 11 3 7 26 21 5 1 8 13 8 10
## [116] 9 26 4 21 16 22 28 4 15 30 17 23 22 3 24 16 20 1 27 16 11 10 17
## [139] 25 12 12 23 23 23 16 23 7 15 22 16 15 27 27 23 8 18 22 14 15 18 15
## [162] 24 28 24 15 22 23 15 17 26 27 23 15 14 22 23 17 8 24 15 4 22 22 6
## [185] 14 27 22 6
##
## Within cluster sum of squares by cluster:
## [1] 34356773 105771689 88597455 314184114 127833336 159760433
## [7] 59143136 181715591 435839538 1054398522 542853822 743250681
## [13] 158926527 45791602 318211556 426898065 137983676 33821294
## [19] 2999979102 61529921 48926147 305229964 468380699 238906699
## [25] 69723267 482848489 161167539 65683793 49915474 158051894
## (between_SS / total_SS = 97.6 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss"
## [5] "tot.withinss" "betweenss" "size" "iter"
## [9] "ifault"