Kmeans
K Means Clustering Project Usually when dealing with an unsupervised learning problem, its difficult to get a good measure of how well the model performed. For this project, we will use data from the UCI archive based off of red and white wines (this is a very commonly used data set in ML).
We will then add a label to the a combined data set, we’ll bring this label back later to see how well we can cluster the wine into groups.
Get the Data
library(readr)
df1 <- read.csv('winequality-red.csv',sep=';')
df2 <- read.csv('winequality-white.csv',sep=';')Checking the head of both dataset
df1$label <- sapply(df1$pH,function(x){'red'})
df2$label <- sapply(df2$pH,function(x){'white'})head(df1)## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 7.4 0.70 0.00 1.9 0.076
## 2 7.8 0.88 0.00 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.70 0.00 1.9 0.076
## 6 7.4 0.66 0.00 1.8 0.075
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
## 1 11 34 0.9978 3.51 0.56 9.4
## 2 25 67 0.9968 3.20 0.68 9.8
## 3 15 54 0.9970 3.26 0.65 9.8
## 4 17 60 0.9980 3.16 0.58 9.8
## 5 11 34 0.9978 3.51 0.56 9.4
## 6 13 40 0.9978 3.51 0.56 9.4
## quality label
## 1 5 red
## 2 5 red
## 3 5 red
## 4 6 red
## 5 5 red
## 6 5 redhead(df2)## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 7.0 0.27 0.36 20.7 0.045
## 2 6.3 0.30 0.34 1.6 0.049
## 3 8.1 0.28 0.40 6.9 0.050
## 4 7.2 0.23 0.32 8.5 0.058
## 5 7.2 0.23 0.32 8.5 0.058
## 6 8.1 0.28 0.40 6.9 0.050
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
## 1 45 170 1.0010 3.00 0.45 8.8
## 2 14 132 0.9940 3.30 0.49 9.5
## 3 30 97 0.9951 3.26 0.44 10.1
## 4 47 186 0.9956 3.19 0.40 9.9
## 5 47 186 0.9956 3.19 0.40 9.9
## 6 30 97 0.9951 3.26 0.44 10.1
## quality label
## 1 6 white
## 2 6 white
## 3 6 white
## 4 6 white
## 5 6 white
## 6 6 whitewine <- rbind(df1,df2)str(wine)## 'data.frame': 6497 obs. of 13 variables:
## $ fixed.acidity : num 7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ...
## $ volatile.acidity : num 0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ...
## $ citric.acid : num 0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ...
## $ residual.sugar : num 1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ...
## $ chlorides : num 0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.071 ...
## $ free.sulfur.dioxide : num 11 25 15 17 11 13 15 15 9 17 ...
## $ total.sulfur.dioxide: num 34 67 54 60 34 40 59 21 18 102 ...
## $ density : num 0.998 0.997 0.997 0.998 0.998 ...
## $ pH : num 3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.35 ...
## $ sulphates : num 0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.8 ...
## $ alcohol : num 9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 10.5 ...
## $ quality : int 5 5 5 6 5 5 5 7 7 5 ...
## $ label : chr "red" "red" "red" "red" ...EDA
library(ggplot2)
pl <- ggplot(wine,aes(x=residual.sugar)) + geom_histogram(aes(fill=label),color='black',bins=50)
pl + scale_fill_manual(values = c('#ae4554','#F2E4B7')) + theme_bw()
ggplot(wine,aes(citric.acid))+geom_histogram(bins = 50,aes(fill=label),color='black')+
scale_fill_manual(values = c('#ae4554','#F2E4B7'))
ggplot(wine,aes(alcohol))+geom_histogram(bins = 50,aes(fill=label),color='black')+
scale_fill_manual(values = c('#ae4554','#F2E4B7'))
ggplot(wine,aes(citric.acid,residual.sugar))+geom_point(alpha=0.2,aes(color=label))+theme_dark()+
scale_color_manual(values = c('#ae4554','#faf7ea'))
ggplot(wine,aes(volatile.acidity,residual.sugar))+geom_point(alpha=0.2,aes(color=label))+theme_dark()+
scale_color_manual(values = c('#ae4554','#faf7ea'))
Creating the clus.data without the lable column
clus.data <- wine[,1:12]
head(clus.data)## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 7.4 0.70 0.00 1.9 0.076
## 2 7.8 0.88 0.00 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.70 0.00 1.9 0.076
## 6 7.4 0.66 0.00 1.8 0.075
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
## 1 11 34 0.9978 3.51 0.56 9.4
## 2 25 67 0.9968 3.20 0.68 9.8
## 3 15 54 0.9970 3.26 0.65 9.8
## 4 17 60 0.9980 3.16 0.58 9.8
## 5 11 34 0.9978 3.51 0.56 9.4
## 6 13 40 0.9978 3.51 0.56 9.4
## quality
## 1 5
## 2 5
## 3 5
## 4 6
## 5 5
## 6 5Buliding the clusters
wine.cluster <-kmeans(clus.data,2)
print(wine.cluster)## K-means clustering with 2 clusters of sizes 2808, 3689
##
## Cluster means:
## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 7.623219 0.4086378 0.2908725 3.076425 0.06580983
## 2 6.904812 0.2871659 0.3397642 7.244809 0.04859257
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates
## 1 18.39868 63.26318 0.9945736 3.254882 0.5724145
## 2 39.75590 155.69246 0.9947903 3.190808 0.4999485
## alcohol quality
## 1 10.79722 5.810541
## 2 10.25932 5.824343
##
## Clustering vector:
## [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1
## [38] 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 2 1 1 1 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1
## [75] 1 1 1 1 1 2 1 1 1 1 1 1 2 1 2 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1
## [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1
## [149] 1 1 1 1 1 1 2 2 2 2 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [186] 1 1 1 2 2 2 1 2 1 1 2 1 1 1 1 1 2 1 1 1 1 1 2 1 1 1 1 1 1 1 2 1 1 1 2 1 1
## [223] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1
## [260] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [297] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2
## [334] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [371] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 2 1 1 1 1 1 1
## [408] 1 1 1 1 1 1 1 2 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [445] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [482] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1
## [519] 1 1 1 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [556] 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2
## [593] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [630] 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [667] 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1
## [704] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [741] 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1
## [778] 1 1 1 1 1 1 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [815] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [852] 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [889] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [926] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [963] 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1000] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1037] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1074] 1 1 1 1 1 1 2 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1111] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1148] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1185] 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1222] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1259] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1296] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1333] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1370] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 2 2 1 1 1 1
## [1407] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1444] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1481] 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 1 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1518] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1555] 1 1 1 1 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [1592] 1 1 1 1 1 1 1 1 2 2 1 2 2 1 2 2 2 2 1 1 1 2 2 2 1 1 2 2 1 1 2 2 2 2 2 2 2
## [1629] 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 2 1 2 2
## [1666] 2 2 1 1 2 2 2 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2
## [1703] 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 1 1
## [1740] 1 2 1 1 1 2 2 1 1 2 2 2 2 1 1 2 2 2 2 2 2 2 1 2 2 2 2 1 2 1 2 1 1 2 2 2 1
## [1777] 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 2 2
## [1814] 1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 1 1 2 2
## [1851] 2 2 1 2 2 1 1 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 2 2
## [1888] 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 1 2 2 2 2
## [1925] 2 2 2 2 1 1 2 1 2 1 1 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1
## [1962] 2 2 1 2 2 2 2 1 2 2 2 2 1 1 2 1 2 2 1 2 2 2 2 1 2 2 2 2 2 1 2 1 2 2 1 1 2
## [1999] 1 1 2 1 2 2 2 1 2 2 1 2 2 1 1 2 2 1 2 1 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 1 1
## [2036] 2 2 2 1 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 1 2 2 2 2 1
## [2073] 2 1 2 1 2 2 2 2 1 2 2 2 2 2 1 2 2 1 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [2110] 2 2 2 1 2 2 2 2 1 1 2 2 1 1 1 2 1 2 1 2 1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2
## [2147] 1 2 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 2 1
## [2184] 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 1 2 1 2 2 2 2 1 2 2 2 2
## [2221] 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2
## [2258] 2 2 1 2 2 1 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [2295] 2 1 2 2 2 2 2 1 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 2 2 2
## [2332] 1 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 2
## [2369] 2 2 2 2 2 2 1 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 1 2 2 2 2 2 2
## [2406] 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 1 2 1 2 2 2 1 1 2 2 1 1 2 1 2 2 2 2 2 2 2
## [2443] 1 2 2 2 1 2 1 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 1 1 2 2 1 2 1
## [2480] 2 2 2 2 2 1 2 1 2 1 2 2 2 2 2 1 2 1 1 2 2 2 2 2 2 1 1 2 2 2 2 2 2 1 1 1 2
## [2517] 2 1 1 2 2 2 2 2 1 1 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 1 2 2 1 2 2 1 1 2 2 1 2
## [2554] 2 2 2 1 1 2 2 1 2 2 2 1 2 2 1 1 1 1 2 1 2 1 2 2 2 1 1 2 1 1 2 2 2 2 2 2 2
## [2591] 1 2 1 2 2 1 2 2 2 1 2 2 2 2 1 2 1 2 2 1 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1
## [2628] 2 2 2 2 2 2 2 2 1 1 1 1 2 1 1 2 1 1 1 1 1 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2
## [2665] 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2
## [2702] 2 2 2 2 2 1 1 2 1 1 2 1 1 1 1 2 1 2 1 2 1 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2
## [2739] 1 1 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 1 2 1 2 1 2 2 2 2 1 2 2
## [2776] 2 2 2 1 2 2 1 2 2 2 2 1 2 1 2 2 1 2 2 2 2 2 1 1 1 1 2 1 1 2 2 2 2 2 1 2 2
## [2813] 1 1 2 1 2 2 1 2 2 2 2 1 1 1 1 1 1 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 1 2
## [2850] 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [2887] 2 1 1 1 2 1 1 1 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 1 2 1 2 2 2
## [2924] 2 2 2 2 1 1 1 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2
## [2961] 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 1 2 2 2 1 1 1 2 1 2 2
## [2998] 2 1 2 2 2 2 1 2 1 1 2 2 1 1 2 2 2 1 2 2 2 1 2 1 1 2 2 2 2 2 1 1 2 1 1 1 2
## [3035] 1 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2
## [3072] 1 2 1 2 2 1 2 2 2 2 2 1 2 2 1 2 2 2 2 1 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2
## [3109] 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 1 1 2 1 1 1 1 2
## [3146] 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 1 2 2 1 2 2 1 2 2 2 2 2 2 1 2 1 2 2 2 2 1 2
## [3183] 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1 1 2 2 1 1 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2
## [3220] 1 2 2 2 2 2 2 2 1 1 2 1 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 1
## [3257] 2 2 2 2 2 2 2 2 2 1 2 1 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [3294] 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 1 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 1 2 2 2 1
## [3331] 2 2 2 2 2 1 2 1 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 1 2 2 2 1 2 2 2 1 2 2 2 2 2
## [3368] 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2
## [3405] 2 2 2 2 2 2 2 1 1 1 2 2 2 1 2 2 1 1 2 2 1 2 2 2 2 2 2 2 2 2 2 1 1 2 1 2 1
## [3442] 2 2 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2
## [3479] 1 2 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 1 2 2 2 2 1 2 2 1 2 2 2 1
## [3516] 2 1 2 2 2 2 2 1 1 1 1 2 2 2 2 2 1 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [3553] 2 2 2 1 1 2 1 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1
## [3590] 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 1 2 2 2 1 1 2 1 2 2 2 2 2 2 2 2 1
## [3627] 2 2 2 2 2 1 2 2 1 2 2 1 1 2 1 1 1 2 1 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 1 2 2
## [3664] 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2
## [3701] 1 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [3738] 1 1 2 2 2 1 2 2 1 2 1 1 1 2 1 1 2 2 1 1 1 1 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2
## [3775] 2 2 2 2 1 2 2 2 2 1 1 1 2 1 2 2 2 2 2 1 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [3812] 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1
## [3849] 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 1 2 2 2 1 2 2 1 1 2 2 2 2
## [3886] 2 2 2 1 1 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2
## [3923] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 1 2 2 1 2 2 2 2 2 1 1 2 2 1 2 2
## [3960] 2 1 2 2 2 2 2 2 2 2 1 2 1 1 2 2 2 1 2 2 2 2 1 1 1 2 2 2 1 1 2 2 2 2 2 2 2
## [3997] 1 1 1 1 1 2 2 2 2 1 1 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
## [4034] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 1 2 2
## [4071] 2 1 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2
## [4108] 2 2 2 2 1 2 2 1 2 2 1 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 1 2 1 2 2 1 2 2 2 1 2
## [4145] 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 1 2 1 2 2 2 1 2 2 2 2 1 1 2 2 2 2 2 1 2 2 2
## [4182] 2 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 1 1 2 2 2 1 2 2 1 2 1 2 2 2 2
## [4219] 2 2 2 2 2 1 2 2 2 2 2 1 1 2 2 2 1 2 2 1 1 1 2 1 2 2 2 1 2 2 2 2 2 1 2 2 2
## [4256] 2 2 2 1 2 2 2 1 1 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 2
## [4293] 2 1 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 1 2 1 2 2 2 1 2 2 1
## [4330] 2 2 2 1 2 2 2 1 2 1 2 2 2 1 1 1 2 2 2 2 2 2 2 1 1 2 2 1 1 2 2 2 2 2 2 2 1
## [4367] 2 2 2 2 2 2 1 2 2 2 2 1 1 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2 1 1 2 2 2 2 2 1 1
## [4404] 1 2 2 2 2 2 1 2 1 2 1 1 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1 1
## [4441] 1 1 1 1 1 1 1 2 2 2 1 2 1 1 2 2 1 2 2 2 1 1 1 1 2 2 2 2 1 2 1 2 1 2 1 2 2
## [4478] 2 1 1 1 2 1 2 1 1 1 1 2 2 2 2 2 1 2 2 2 1 2 1 1 2 1 2 2 2 1 1 1 2 2 1 2 1
## [4515] 1 2 2 1 2 1 2 2 2 2 2 1 2 2 2 2 1 2 2 1 1 1 2 1 1 2 1 2 1 2 2 1 1 2 2 1 1
## [4552] 1 1 1 2 1 1 1 1 2 2 1 2 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 2 1
## [4589] 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 1 1 1 1 1 1 2 2 1 1 1 2 1 1 2
## [4626] 2 2 2 2 2 2 1 2 2 1 2 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 1 1 2 1 2 2 1 2 2 2
## [4663] 2 2 2 2 2 2 1 2 1 2 2 2 1 2 2 1 2 1 2 1 1 1 1 1 2 1 1 1 2 2 2 1 1 1 2 2 2
## [4700] 2 1 2 2 2 2 2 2 2 2 1 1 2 2 2 2 1 1 2 1 2 2 1 1 2 2 2 1 1 2 2 2 2 2 2 2 1
## [4737] 2 2 2 2 1 2 1 2 2 2 2 2 2 1 2 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 1 2 2 1 2 2
## [4774] 2 2 1 2 1 1 1 1 2 1 1 2 1 2 2 2 2 2 1 2 1 2 2 2 2 2 2 2 1 2 2 2 1 2 2 2 1
## [4811] 2 2 2 1 1 2 1 1 1 1 1 2 2 2 1 2 2 2 2 2 1 1 1 2 2 2 2 2 1 2 2 2 1 1 2 2 2
## [4848] 2 2 2 2 1 1 2 2 2 2 2 2 2 1 2 1 2 2 1 2 2 2 2 1 1 2 2 1 2 2 2 2 2 2 2 2 1
## [4885] 2 2 2 2 2 1 1 2 1 2 2 2 2 2 1 1 1 1 1 2 1 2 2 2 1 2 2 1 1 2 1 1 1 1 2 2 1
## [4922] 1 1 2 2 2 1 2 2 2 2 2 2 1 2 2 2 1 1 2 1 1 2 2 2 2 2 1 1 2 1 1 1 2 2 2 1 1
## [4959] 1 1 1 2 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 2 2 1 2 2 2
## [4996] 2 2 2 1 2 2 2 1 1 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2
## [5033] 1 1 1 1 2 2 2 1 2 1 1 2 2 2 1 2 2 1 1 2 1 1 1 2 2 2 2 1 2 2 1 2 2 2 2 1 2
## [5070] 2 1 2 1 2 2 1 2 2 1 1 2 1 1 1 2 1 2 1 1 2 2 2 2 2 1 2 2 2 2 2 2 2 1 2 2 2
## [5107] 2 2 2 2 1 2 1 1 1 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 2 2 1 1 2 1 1 2 2 2
## [5144] 2 2 2 2 2 2 2 2 2 2 1 2 1 1 2 1 1 1 2 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 1 2
## [5181] 2 1 1 1 1 2 2 2 2 1 2 2 2 1 2 2 2 2 2 1 2 2 2 1 2 2 1 2 2 2 1 2 2 2 1 2 2
## [5218] 2 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 1 1 2 1 1 2 2 2 2 2 2 1 1 2 2 2 2 1 2 1
## [5255] 2 2 2 2 2 2 2 1 2 2 2 2 2 1 1 2 1 1 1 2 2 1 2 1 1 2 2 2 2 2 2 2 1 2 2 2 2
## [5292] 1 2 1 2 2 2 1 2 2 2 2 2 2 2 1 2 2 2 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 2 1
## [5329] 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1 2 2 2 1 1 1 1 2 2
## [5366] 2 2 2 2 2 2 2 1 2 1 2 1 1 1 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 1 1 2 2 1 1 2
## [5403] 1 1 1 1 1 1 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 1 2 1 2 1 2 2 2 2 2 2 1 1 2 2
## [5440] 1 2 2 1 1 2 1 2 1 1 1 2 1 2 2 2 1 1 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 1 2
## [5477] 1 2 1 2 2 2 2 2 1 2 1 2 2 1 2 2 1 2 1 2 2 2 2 1 1 1 1 1 1 1 1 2 2 2 2 1 1
## [5514] 2 1 2 2 2 1 2 2 1 1 1 1 1 1 2 2 1 1 2 1 1 2 1 2 1 2 2 2 1 2 2 2 2 2 1 2 2
## [5551] 2 1 2 2 1 2 1 2 2 2 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 1 1 2
## [5588] 2 1 2 1 1 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 1 2 2 2 2 2 2 1 2 2 2 1 1 1 2 2 2
## [5625] 1 2 2 1 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 2 1 2 2 2 1 2 2 2 1 1 1
## [5662] 2 2 1 2 2 2 2 2 2 1 2 1 1 2 2 1 1 1 2 2 2 2 1 1 1 1 1 2 2 1 2 1 1 1 2 1 2
## [5699] 2 1 2 2 1 1 2 2 2 1 2 2 1 1 1 1 1 2 2 2 2 2 2 2 1 1 2 2 2 2 1 2 2 2 2 1 2
## [5736] 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 2 1 2
## [5773] 1 2 2 2 2 2 2 2 1 2 1 1 2 1 2 2 1 1 2 1 1 1 1 1 2 2 1 1 1 2 2 2 1 2 2 1 1
## [5810] 2 2 2 1 2 2 2 2 2 2 1 2 2 1 1 2 2 1 2 2 2 1 1 2 2 2 2 1 2 1 2 2 2 1 2 2 1
## [5847] 2 2 2 1 1 1 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 1
## [5884] 1 1 1 2 1 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 1 2 2 1 2 1 2 2 1 2 2 2 2
## [5921] 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 1 1 1 2 2 2 1 2 2 2 2 2 2 2
## [5958] 2 2 2 2 2 2 2 2 2 2 1 2 2 1 2 1 2 2 2 1 2 2 2 2 2 1 2 2 2 1 1 2 2 2 2 2 2
## [5995] 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1
## [6032] 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 2 2 1
## [6069] 1 1 1 1 2 2 2 1 2 2 2 2 2 2 1 2 1 1 1 2 2 2 1 1 2 1 2 1 2 1 1 2 2 2 1 2 2
## [6106] 1 1 2 1 2 1 1 1 2 2 1 1 1 2 2 2 1 2 2 2 2 1 1 2 2 2 2 2 1 2 2 1 2 1 1 1 1
## [6143] 1 1 1 1 1 2 1 1 2 1 1 1 1 2 2 2 1 1 1 2 2 2 2 2 2 2 2 1 1 1 1 1 2 1 1 1 2
## [6180] 2 2 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 1 2 1 2 2 2 2 2 1 1 2 1 1 1 1 2 2 2 2 1
## [6217] 2 2 2 2 1 2 2 1 2 2 1 1 1 2 1 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 1 2 1 1 2 2 1
## [6254] 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 1 2 2 2 1 1 2 2 2 2 2 2 2 2
## [6291] 2 2 2 2 2 1 2 2 2 2 2 2 1 2 2 2 2 1 2 1 2 2 1 2 1 1 2 2 1 1 1 2 2 1 2 1 2
## [6328] 2 1 1 2 2 2 1 1 1 2 2 1 2 2 2 2 1 2 2 1 2 2 2 2 1 1 1 1 2 2 2 2 2 2 1 2 2
## [6365] 1 2 2 2 2 2 2 2 1 2 2 2 1 1 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2
## [6402] 2 2 1 1 2 1 1 2 2 1 2 2 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 1 2 2 2 1 1 1 1 2 2
## [6439] 1 2 2 1 2 2 1 2 1 2 2 2 2 1 1 1 2 2 2 2 2 2 1 1 1 2 1 2 1 2 1 2 1 2 2 2 1
## [6476] 2 1 1 2 2 2 2 2 2 2 2 1 2 2 1 2 2 1 2 2 1 1
##
## Within cluster sum of squares by cluster:
## [1] 3256954 5337874
## (between_SS / total_SS = 62.6 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"print(wine.cluster$centers)## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 7.623219 0.4086378 0.2908725 3.076425 0.06580983
## 2 6.904812 0.2871659 0.3397642 7.244809 0.04859257
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates
## 1 18.39868 63.26318 0.9945736 3.254882 0.5724145
## 2 39.75590 155.69246 0.9947903 3.190808 0.4999485
## alcohol quality
## 1 10.79722 5.810541
## 2 10.25932 5.824343Evaluating the Clusters
table(wine$label,wine.cluster$cluster)##
## 1 2
## red 1514 85
## white 1294 3604We can see that red is easier to cluster together, which makes sense given our previous visualizations. There seems to be a lot of noise with white wines, this could also be due to “Rose” wines being categorized as white wine, while still retaining the qualities of a red wine. Overall this makes sense since wine is essentially just fermented grape juice and the chemical measurements we were provided may not correlate well with whether or not the wine is red or white!
It’s important to note here, that K-Means can only give you the clusters, it can’t directly tell you what the labels should be, or even how many clusters you should have, we are just lucky to know we expected two types of wine. This is where domain knowledge really comes into play.