Air_Data <- read.csv("D:\\DataScience\\Assignments\\Clustaring\\EastWestAirlines.csv")
View(Air_Data)
Air1<- Air_Data[,-1]
Air <- scale(Air1)
View(Air)
#data Normalization
plot(Air)
km <- kmeans(Air,4) #kmeans clustering
str(km)
## List of 9
## $ cluster : int [1:3999] 4 4 4 4 2 4 2 3 1 2 ...
## $ centers : num [1:4, 1:11] 1.234 0.632 -0.157 -0.3 0.778 ...
## ..- attr(*, "dimnames")=List of 2
## .. ..$ : chr [1:4] "1" "2" "3" "4"
## .. ..$ : chr [1:11] "Balance" "Qual_miles" "cc1_miles" "cc2_miles" ...
## $ totss : num 43978
## $ withinss : num [1:4] 8602 6671 5251 8210
## $ tot.withinss: num 28735
## $ betweenss : num 15243
## $ size : int [1:4] 151 912 832 2104
## $ iter : int 4
## $ ifault : int 0
## - attr(*, "class")= chr "kmeans"
km$centers
## Balance Qual_miles cc1_miles cc2_miles cc3_miles Bonus_miles
## 1 1.2344208 0.77765957 0.2405468 0.17088676 1.36187041 0.9758147
## 2 0.6317661 -0.01183768 1.4809650 -0.08337706 -0.02906215 1.2619693
## 3 -0.1573947 0.11086843 -0.2762895 0.03201582 -0.06275873 -0.2695878
## 4 -0.3001976 -0.09452147 -0.5499476 0.01121617 -0.06032438 -0.5104406
## Bonus_trans Flight_miles_12mo Flight_trans_12 Days_since_enroll
## 1 1.8067560 3.51834178 3.78789227 0.29274276
## 2 0.8396323 -0.06722512 -0.06669335 0.45863789
## 3 -0.0943362 -0.02689278 -0.02244345 0.05671985
## 4 -0.4563104 -0.21273075 -0.23406580 -0.24223994
## Award.
## 1 0.9335192
## 2 0.4342030
## 3 1.2938004
## 4 -0.7668234
km$cluster
## [1] 4 4 4 4 2 4 2 3 1 2 4 2 4 4 4 2 2 3 2 3 2 3 4 4 4 4 4 4 2 3 2 4 2 4
## [35] 4 2 3 4 2 3 4 2 3 2 2 3 4 3 2 4 4 4 2 3 4 4 2 3 4 2 2 4 4 4 4 1 4 2
## [69] 2 3 3 2 2 4 2 2 4 2 2 2 3 4 4 4 4 2 3 2 4 3 3 4 2 3 1 4 4 2 4 3 2 3
## [103] 4 3 4 2 1 3 2 2 2 3 1 3 3 2 4 2 2 3 4 2 2 2 2 2 2 1 3 2 3 3 3 3 3 1
## [137] 2 2 3 4 4 4 4 2 4 3 4 2 2 4 2 2 2 4 2 2 2 3 2 4 2 2 3 2 3 4 4 2 2 2
## [171] 2 4 2 3 2 2 3 3 3 3 4 3 4 3 2 4 1 4 2 4 2 1 2 4 3 4 2 2 3 2 2 3 4 2
## [205] 4 4 2 3 3 4 2 4 4 3 4 2 4 4 2 2 1 2 3 4 2 4 3 2 4 2 2 2 4 3 3 3 4 3
## [239] 1 4 2 3 2 1 2 1 4 3 2 4 4 4 2 3 3 2 2 3 4 2 4 4 4 4 3 2 4 4 2 2 2 2
## [273] 2 4 4 1 4 2 4 2 4 2 4 3 2 2 2 4 2 2 2 2 2 4 4 2 2 2 1 4 3 4 2 2 4 2
## [307] 4 1 4 4 2 2 2 2 3 3 4 4 2 3 2 2 2 1 4 3 4 1 2 4 2 4 2 3 2 2 2 4 4 2
## [341] 2 3 4 4 4 2 2 2 2 3 4 2 4 3 2 4 4 2 4 4 3 4 2 4 4 3 3 4 4 2 3 2 2 4
## [375] 3 1 4 2 4 2 1 4 2 2 1 2 4 2 2 4 4 3 3 4 4 4 4 4 4 4 4 4 4 4 1 2 4 3
## [409] 4 4 1 2 1 2 4 3 3 3 2 4 1 4 2 2 2 2 4 2 4 2 3 3 3 2 3 4 2 2 2 4 4 2
## [443] 4 4 2 3 2 4 2 4 2 4 2 2 4 4 3 4 2 2 4 2 3 4 4 2 1 2 4 3 4 1 2 4 4 1
## [477] 4 2 2 2 4 2 3 4 3 3 2 3 1 2 3 4 1 4 2 4 3 3 4 2 1 2 4 2 4 2 2 3 4 4
## [511] 2 1 2 4 2 2 3 2 2 3 4 4 2 4 3 4 2 2 2 3 3 1 2 4 2 3 3 2 4 4 4 3 4 4
## [545] 3 3 2 2 3 1 2 3 4 4 2 3 4 3 4 4 3 2 3 2 2 2 4 2 4 4 3 2 4 4 2 4 2 4
## [579] 3 4 2 4 4 4 4 4 4 2 4 3 4 4 3 2 1 4 4 4 2 2 2 2 2 2 3 3 3 4 2 4 4 1
## [613] 3 3 3 2 4 3 2 2 2 4 1 2 2 2 4 2 4 2 1 4 4 4 4 4 4 3 2 2 3 4 4 4 4 4
## [647] 4 3 4 3 3 3 3 2 4 4 4 2 3 3 2 4 1 3 2 4 4 4 4 2 2 4 2 3 2 3 3 3 2 4
## [681] 4 1 2 1 4 2 4 2 4 2 4 2 2 2 4 4 4 2 4 4 4 2 1 3 2 2 3 4 1 4 3 2 4 3
## [715] 2 4 2 3 1 4 4 3 3 2 4 2 2 4 4 4 2 2 4 4 3 4 2 4 4 2 3 2 4 1 3 2 4 4
## [749] 4 4 2 3 4 3 2 4 2 2 3 3 3 4 4 2 2 2 4 3 3 2 4 4 1 2 2 4 3 4 4 3 2 2
## [783] 3 4 4 4 4 2 2 3 3 2 2 2 4 3 2 2 2 3 2 4 4 3 4 3 2 2 4 4 4 3 2 2 2 3
## [817] 4 4 2 4 3 4 3 2 4 4 3 4 2 3 2 2 2 1 3 2 2 2 4 1 2 2 2 4 2 4 4 2 4 2
## [851] 1 1 4 4 2 2 4 4 2 2 1 2 3 4 2 2 3 4 3 3 3 3 3 4 4 3 4 2 4 4 2 2 4 2
## [885] 2 3 3 2 4 3 4 2 4 2 4 4 3 4 4 4 2 2 4 4 1 2 4 4 4 2 4 2 4 4 1 3 3 2
## [919] 4 2 4 4 2 3 4 3 3 4 4 4 2 4 2 4 2 2 2 2 3 4 3 3 3 4 4 4 2 2 3 4 4 4
## [953] 4 2 2 2 4 4 1 2 4 4 2 2 3 3 1 4 3 2 4 3 2 4 4 2 1 2 4 2 2 4 4 2 2 4
## [987] 3 4 2 4 4 3 2 2 3 2 4 2 2 2 2 3 4 2 4 4 4 3 4 2 3 4 4 3 1 3 3 2 4 4
## [1021] 2 4 3 4 4 3 4 3 4 4 4 4 1 3 4 4 2 2 4 3 4 4 2 3 3 4 3 2 2 2 4 4 2 2
## [1055] 4 2 2 2 2 4 4 1 4 3 1 3 3 2 2 4 2 2 4 4 4 4 2 4 3 3 3 4 4 3 3 2 4 3
## [1089] 4 4 4 4 4 2 3 3 3 4 4 2 4 4 2 2 4 2 2 4 3 4 2 2 2 2 4 2 4 2 4 2 4 4
## [1123] 1 2 2 2 2 4 2 4 2 3 4 4 2 3 3 4 2 4 2 3 4 2 3 3 3 3 3 4 1 4 4 4 2 3
## [1157] 4 2 2 4 3 4 4 2 3 4 4 4 2 2 3 2 2 3 3 4 2 3 2 4 4 2 3 4 4 4 2 1 2 4
## [1191] 4 4 4 2 4 4 3 4 4 4 3 4 4 4 3 4 1 3 2 4 2 4 4 3 4 2 3 4 4 3 3 2 3 4
## [1225] 2 3 2 3 4 4 4 4 3 4 4 4 1 4 4 4 3 4 4 4 1 2 4 3 4 3 2 4 3 4 2 3 3 4
## [1259] 2 4 4 3 1 4 4 3 2 3 2 2 4 3 4 2 4 4 4 3 3 4 4 2 2 2 4 2 4 4 2 3 4 3
## [1293] 3 2 3 2 4 4 1 4 1 4 1 4 4 3 2 4 4 4 2 4 4 2 1 2 3 2 4 3 4 2 3 4 4 2
## [1327] 3 2 3 3 4 4 4 4 4 4 4 2 4 2 4 4 4 4 3 4 4 3 3 4 4 3 2 2 3 4 4 3 2 4
## [1361] 4 4 3 4 2 4 4 4 4 4 3 4 2 4 3 4 4 4 2 2 4 4 2 3 2 2 3 4 3 4 4 4 4 4
## [1395] 2 3 4 2 4 4 4 2 4 4 3 2 2 2 4 4 4 2 4 2 2 2 4 3 2 2 2 4 2 3 4 2 4 4
## [1429] 1 2 2 4 4 1 4 3 2 4 2 4 3 2 4 3 4 4 3 4 4 3 2 3 2 2 4 2 4 4 4 3 4 2
## [1463] 2 2 4 4 4 3 4 3 2 2 4 4 4 4 3 2 3 2 4 2 4 2 4 4 2 4 2 1 4 4 3 3 3 4
## [1497] 2 3 3 4 3 3 4 2 4 2 4 4 4 4 4 4 3 3 4 4 3 3 3 2 3 3 4 3 3 2 2 4 4 1
## [1531] 3 4 4 3 2 4 2 4 3 2 4 4 2 4 4 3 2 1 4 4 2 4 4 4 4 4 3 3 4 4 4 4 4 4
## [1565] 4 3 4 3 3 2 4 4 4 3 4 4 3 3 4 3 4 4 4 3 3 3 4 3 3 2 4 4 2 1 4 1 3 2
## [1599] 4 3 3 4 2 4 4 2 4 4 3 4 4 4 2 2 3 2 2 4 2 2 1 4 2 1 2 4 4 4 2 4 2 4
## [1633] 2 2 4 2 4 3 4 2 4 2 2 4 4 2 4 4 4 4 2 2 4 2 4 3 2 2 3 3 3 3 2 2 4 4
## [1667] 2 3 4 3 4 2 4 2 2 3 1 4 4 3 2 2 4 2 4 4 3 2 2 3 4 2 2 3 3 4 4 4 3 3
## [1701] 3 4 2 2 4 4 2 2 4 4 2 4 4 4 4 3 3 2 2 4 4 2 2 2 2 2 4 2 1 4 2 4 3 2
## [1735] 4 4 2 4 2 4 2 4 4 4 2 4 2 4 4 4 3 2 4 2 4 4 4 4 4 4 3 4 4 2 4 2 2 3
## [1769] 4 4 4 4 3 2 2 3 4 2 4 4 4 2 4 2 3 4 2 4 4 3 4 4 4 4 3 4 4 4 3 4 3 2
## [1803] 3 2 4 3 2 4 3 4 2 4 4 4 4 3 3 2 4 2 4 4 3 4 3 4 4 4 3 4 4 4 4 3 2 4
## [1837] 4 2 2 4 2 2 2 4 4 2 1 4 2 2 4 2 4 4 4 4 4 4 3 4 2 3 4 3 2 4 4 3 3 4
## [1871] 4 2 2 4 2 4 2 2 1 4 4 2 3 4 4 1 4 4 4 4 2 4 3 2 2 4 4 4 4 2 4 4 4 4
## [1905] 4 3 4 2 4 4 2 1 4 3 4 4 2 1 4 2 2 3 3 2 3 4 4 4 4 4 1 4 4 4 2 4 2 4
## [1939] 4 2 4 2 2 2 4 4 1 4 2 2 4 3 4 4 2 3 3 4 2 4 3 3 4 4 4 3 3 4 3 4 4 4
## [1973] 4 4 2 3 4 2 4 4 3 4 4 4 4 4 4 2 4 4 4 3 2 4 4 4 4 2 3 2 4 2 2 2 1 4
## [2007] 4 4 4 2 4 3 2 4 4 1 4 4 3 2 3 2 3 4 2 3 4 4 3 4 4 4 4 2 4 4 4 2 3 3
## [2041] 4 4 4 3 4 4 2 3 4 1 3 2 3 4 4 4 3 4 1 3 4 2 3 3 4 4 3 4 3 2 2 4 2 4
## [2075] 2 4 4 4 2 4 3 4 4 3 4 4 4 2 4 4 4 3 2 3 3 4 4 3 4 4 4 3 4 4 4 2 3 3
## [2109] 4 4 4 4 2 3 3 4 4 4 3 4 4 3 3 2 4 2 4 4 4 4 3 2 2 2 3 4 2 2 4 4 4 4
## [2143] 4 4 4 2 4 3 4 4 4 1 4 1 4 4 3 3 4 4 4 3 2 2 4 4 1 3 4 2 4 3 2 2 4 3
## [2177] 3 2 4 1 3 2 3 4 4 2 2 3 4 3 4 4 4 3 3 4 2 4 3 2 4 2 3 4 3 4 3 4 4 3
## [2211] 4 3 3 3 3 4 3 1 3 4 4 4 3 4 4 1 3 4 4 4 2 4 3 4 4 4 4 4 4 4 3 3 3 2
## [2245] 4 3 2 4 4 4 1 1 2 4 3 4 4 3 4 4 4 2 4 3 3 4 3 1 4 2 3 4 2 2 3 2 2 3
## [2279] 4 4 4 3 4 2 4 4 4 2 2 4 3 4 3 3 2 2 4 3 4 4 4 3 4 4 2 2 4 2 2 3 3 2
## [2313] 4 4 4 4 2 4 2 2 4 4 3 2 4 4 4 3 3 4 4 3 2 4 2 4 3 4 3 3 4 3 4 2 3 3
## [2347] 3 4 4 4 3 2 2 4 4 2 4 2 2 2 4 4 2 4 1 4 2 3 4 4 4 4 4 3 4 1 2 4 2 3
## [2381] 4 3 4 3 4 4 4 3 4 4 3 4 2 4 4 4 4 4 3 3 4 3 2 2 4 4 4 4 4 4 4 2 3 1
## [2415] 3 4 3 2 2 4 2 4 2 4 4 3 4 3 2 4 4 4 4 4 2 2 3 2 3 3 4 2 4 2 4 2 2 4
## [2449] 1 1 4 4 2 4 2 2 2 4 3 4 4 2 2 3 4 2 4 3 4 3 4 4 2 2 2 2 4 3 4 3 4 3
## [2483] 2 4 4 4 3 2 4 2 3 4 3 3 4 4 4 4 4 3 2 1 4 3 3 3 2 4 3 3 3 4 3 2 3 4
## [2517] 4 4 3 2 4 4 1 4 4 2 2 4 3 4 4 2 3 2 3 2 4 4 4 2 4 3 4 2 4 4 4 4 2 2
## [2551] 4 4 4 3 3 4 4 3 4 2 3 2 4 4 3 4 4 3 4 4 3 4 4 3 4 4 4 4 4 2 4 4 4 3
## [2585] 4 3 3 4 4 2 4 2 4 3 2 4 4 4 4 3 3 4 4 3 2 3 4 3 2 4 3 3 2 4 3 2 4 4
## [2619] 4 4 3 3 4 4 4 3 4 4 4 4 4 4 4 3 4 3 4 3 2 2 4 4 3 4 3 3 3 2 4 4 3 2
## [2653] 4 3 4 4 1 2 4 3 4 3 4 4 4 2 4 4 4 4 2 3 1 1 2 4 4 4 4 4 4 4 3 3 3 2
## [2687] 4 4 4 3 3 3 4 2 3 4 1 4 4 4 4 4 4 4 4 4 4 3 3 1 4 2 4 4 3 2 3 4 3 4
## [2721] 4 3 3 4 4 3 3 3 2 2 2 2 3 4 4 4 4 3 3 4 2 4 3 2 4 4 4 1 4 4 4 1 3 3
## [2755] 3 4 4 4 3 4 3 3 4 4 3 4 2 4 4 4 4 2 3 3 4 1 4 4 4 4 2 4 4 2 4 4 4 4
## [2789] 4 1 4 3 3 4 3 4 4 4 4 4 4 3 1 3 4 4 2 4 4 4 4 4 4 3 4 2 4 3 4 2 2 2
## [2823] 2 3 4 4 4 4 4 3 4 2 4 4 4 3 3 4 4 2 2 4 4 4 4 3 4 4 3 2 3 4 3 2 2 3
## [2857] 3 4 4 4 4 4 3 4 3 4 3 4 4 4 3 4 3 4 4 4 4 4 3 4 4 4 2 4 4 4 4 4 1 3
## [2891] 4 3 2 4 4 2 3 3 4 2 4 3 4 1 4 2 4 4 4 4 4 4 4 3 3 4 4 4 2 3 4 4 3 4
## [2925] 4 2 4 2 4 4 4 4 2 3 2 4 4 4 2 4 4 4 3 4 4 4 4 4 4 4 3 4 4 4 3 1 3 3
## [2959] 4 4 4 4 2 4 3 4 4 3 4 4 4 4 4 4 3 4 4 4 3 4 4 4 4 3 4 2 4 3 4 4 2 4
## [2993] 3 3 2 3 3 4 4 1 3 2 2 4 4 3 3 4 4 4 4 3 4 3 2 2 2 3 3 4 2 2 4 4 4 4
## [3027] 3 3 4 4 2 1 2 4 4 3 3 3 4 4 1 4 2 3 4 4 4 4 4 3 4 2 4 3 4 4 4 3 3 4
## [3061] 4 3 2 2 4 4 4 4 4 2 3 4 4 2 4 3 2 4 4 2 3 4 1 4 3 4 4 4 4 4 4 4 3 1
## [3095] 4 4 1 4 3 4 4 3 4 2 1 1 2 4 3 4 4 4 4 2 4 4 4 4 3 3 4 4 4 4 4 4 3 2
## [3129] 4 3 4 4 4 3 4 4 2 2 3 4 4 4 4 4 4 4 2 2 2 2 4 4 4 4 2 4 3 3 3 4 1 4
## [3163] 3 4 4 1 4 3 2 3 4 3 4 3 3 3 4 3 4 4 3 4 4 3 4 2 4 4 4 4 4 4 4 3 3 4
## [3197] 4 3 4 2 4 4 4 4 4 4 4 4 3 4 4 4 3 4 2 4 4 2 3 2 4 4 4 4 4 3 4 4 4 4
## [3231] 3 4 4 4 4 1 3 3 4 4 4 3 4 4 3 4 4 4 4 1 3 4 3 4 4 1 3 4 4 4 1 2 3 3
## [3265] 4 4 4 4 4 4 3 4 4 4 4 4 4 4 2 3 4 3 1 4 4 3 4 4 4 4 3 4 4 2 3 4 4 4
## [3299] 2 4 4 4 3 4 2 4 4 4 4 3 4 2 3 4 2 2 4 4 4 4 3 4 4 4 3 4 3 4 4 2 4 3
## [3333] 3 4 4 3 4 4 1 3 3 4 4 4 4 4 2 4 4 3 4 4 2 4 3 4 2 4 4 4 4 4 2 4 4 4
## [3367] 2 4 4 3 4 4 4 4 4 3 4 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 2 4 3
## [3401] 4 3 4 3 4 3 4 4 4 4 4 2 4 4 4 4 4 2 4 4 4 4 4 4 2 2 4 4 4 1 4 4 4 4
## [3435] 4 4 4 4 2 4 4 4 4 4 2 4 4 2 4 4 3 4 3 4 4 4 4 4 4 4 4 3 3 1 4 4 4 3
## [3469] 4 4 4 4 4 4 4 4 4 3 4 4 4 4 4 4 3 2 4 4 2 4 2 4 4 3 4 4 4 2 3 4 4 2
## [3503] 4 4 4 4 4 3 3 4 4 4 4 4 4 4 4 2 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [3537] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 2 2
## [3571] 4 4 4 4 4 4 4 4 4 4 4 4 4 1 4 4 4 4 4 4 4 4 4 4 1 4 4 4 4 4 4 2 4 4
## [3605] 4 3 4 4 4 3 4 4 3 4 4 4 4 2 4 4 4 4 4 4 3 4 4 4 4 4 4 4 4 4 4 4 3 4
## [3639] 4 4 4 4 3 4 4 4 2 4 4 3 4 4 4 4 4 4 4 3 4 4 4 3 4 4 4 3 4 4 4 4 4 4
## [3673] 4 4 4 4 4 4 4 4 4 3 4 4 4 4 4 4 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [3707] 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
## [3741] 3 4 4 4 4 4 4 4 3 4 4 3 3 2 3 3 3 4 4 4 3 3 3 4 4 4 4 4 4 2 4 4 2 4
## [3775] 4 2 4 3 4 4 4 4 3 3 2 2 4 3 1 2 4 2 2 4 4 4 2 4 3 4 3 2 4 4 3 4 3 4
## [3809] 4 4 4 3 4 4 4 4 3 4 3 2 4 4 2 2 1 4 4 4 4 3 3 3 1 4 2 4 4 3 4 4 4 4
## [3843] 4 2 4 4 1 4 4 4 4 3 4 4 4 2 3 4 4 4 2 4 3 3 4 4 4 2 4 3 4 3 4 4 4 4
## [3877] 4 3 4 4 4 2 4 2 4 4 2 4 3 4 3 4 4 4 4 4 4 3 4 2 3 4 3 4 3 4 4 1 2 4
## [3911] 4 1 4 3 4 3 4 1 4 4 4 3 4 3 4 4 4 4 4 3 2 3 3 4 1 4 3 4 4 4 4 3 4 4
## [3945] 3 4 4 4 4 4 1 4 4 3 3 4 4 4 4 4 4 4 4 4 4 4 2 4 4 4 4 4 4 3 2 4 4 4
## [3979] 3 3 4 4 4 4 4 2 4 3 4 3 4 4 4 4 3 3 3 4 4
finalmodel <- data.frame(km$cluster,Air1)
View(finalmodel)
x <- aggregate(Air1[,1:11],by=list(km$cluster),FUN=mean)
x
## Group.1 Balance Qual_miles cc1_miles cc2_miles cc3_miles Bonus_miles
## 1 1 198000.90 745.76159 2.390728 1.039735 1.278146 40711.715
## 2 2 137267.98 134.95614 4.098684 1.002193 1.006579 47622.625
## 3 3 57739.77 229.88942 1.679087 1.019231 1.000000 10634.041
## 4 4 43348.71 70.98669 1.302281 1.016160 1.000475 4817.212
## Bonus_trans Flight_miles_12mo Flight_trans_12 Days_since_enroll
## 1 28.953642 5386.4702 15.7417219 4723.113
## 2 19.665570 365.9265 1.1206140 5065.708
## 3 10.695913 422.4002 1.2884615 4235.694
## 4 7.219582 162.1882 0.4857414 3618.301
## Award.
## 1 0.8211921
## 2 0.5800439
## 3 0.9951923
## 4 0.0000000