K-means Clustering on Wine Data
Stephen Situ
2022-11-30
This is a project on conducting K-means clustering on wine data. The wine data has various numeric properties like: Alcohol, Malic acid, Ash, Alcalinity of ash, Magnesium, Total phenols, Flavanoids,Nonflavanoid phenols, Proanthocyanins, Color intensity, Hue, OD280/OD315 of diluted wines, Proline
Original Dataset here: https://www.kaggle.com/datasets/harrywang/wine-dataset-for-clustering
K-means clustering is a common unsupervised machine learning technique to group data together. Applications include: Customer Segmentation, Identifying crime localities, Rideshare data analysis, Cyber-Profiling criminals, etc. We set a number of clusters k, which is the number of centroids. A centroid is the center of the cluster. It Initializes on randomly selected centroids and then performs iterative calculations to optimize the position of the centroids. Each data point is assigned to each cluster through reducing the in-cluster sum of squares.
Install Packages
install.packages('tidyverse', repos = "http://cran.us.r-project.org")## Installing package into 'C:/Users/Steve/AppData/Local/R/win-library/4.2'
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library(ggplot2)
library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBaRead CSV
wine_data <- read.csv('wine-clustering.csv')Explore
head(wine_data)## Alcohol Malic_Acid Ash Ash_Alcanity Magnesium Total_Phenols Flavanoids
## 1 14.23 1.71 2.43 15.6 127 2.80 3.06
## 2 13.20 1.78 2.14 11.2 100 2.65 2.76
## 3 13.16 2.36 2.67 18.6 101 2.80 3.24
## 4 14.37 1.95 2.50 16.8 113 3.85 3.49
## 5 13.24 2.59 2.87 21.0 118 2.80 2.69
## 6 14.20 1.76 2.45 15.2 112 3.27 3.39
## Nonflavanoid_Phenols Proanthocyanins Color_Intensity Hue OD280 Proline
## 1 0.28 2.29 5.64 1.04 3.92 1065
## 2 0.26 1.28 4.38 1.05 3.40 1050
## 3 0.30 2.81 5.68 1.03 3.17 1185
## 4 0.24 2.18 7.80 0.86 3.45 1480
## 5 0.39 1.82 4.32 1.04 2.93 735
## 6 0.34 1.97 6.75 1.05 2.85 1450str(wine_data)## 'data.frame': 178 obs. of 13 variables:
## $ Alcohol : num 14.2 13.2 13.2 14.4 13.2 ...
## $ Malic_Acid : num 1.71 1.78 2.36 1.95 2.59 1.76 1.87 2.15 1.64 1.35 ...
## $ Ash : num 2.43 2.14 2.67 2.5 2.87 2.45 2.45 2.61 2.17 2.27 ...
## $ Ash_Alcanity : num 15.6 11.2 18.6 16.8 21 15.2 14.6 17.6 14 16 ...
## $ Magnesium : int 127 100 101 113 118 112 96 121 97 98 ...
## $ Total_Phenols : num 2.8 2.65 2.8 3.85 2.8 3.27 2.5 2.6 2.8 2.98 ...
## $ Flavanoids : num 3.06 2.76 3.24 3.49 2.69 3.39 2.52 2.51 2.98 3.15 ...
## $ Nonflavanoid_Phenols: num 0.28 0.26 0.3 0.24 0.39 0.34 0.3 0.31 0.29 0.22 ...
## $ Proanthocyanins : num 2.29 1.28 2.81 2.18 1.82 1.97 1.98 1.25 1.98 1.85 ...
## $ Color_Intensity : num 5.64 4.38 5.68 7.8 4.32 6.75 5.25 5.05 5.2 7.22 ...
## $ Hue : num 1.04 1.05 1.03 0.86 1.04 1.05 1.02 1.06 1.08 1.01 ...
## $ OD280 : num 3.92 3.4 3.17 3.45 2.93 2.85 3.58 3.58 2.85 3.55 ...
## $ Proline : int 1065 1050 1185 1480 735 1450 1290 1295 1045 1045 ...summary(wine_data)## Alcohol Malic_Acid Ash Ash_Alcanity
## Min. :11.03 Min. :0.740 Min. :1.360 Min. :10.60
## 1st Qu.:12.36 1st Qu.:1.603 1st Qu.:2.210 1st Qu.:17.20
## Median :13.05 Median :1.865 Median :2.360 Median :19.50
## Mean :13.00 Mean :2.336 Mean :2.367 Mean :19.49
## 3rd Qu.:13.68 3rd Qu.:3.083 3rd Qu.:2.558 3rd Qu.:21.50
## Max. :14.83 Max. :5.800 Max. :3.230 Max. :30.00
## Magnesium Total_Phenols Flavanoids Nonflavanoid_Phenols
## Min. : 70.00 Min. :0.980 Min. :0.340 Min. :0.1300
## 1st Qu.: 88.00 1st Qu.:1.742 1st Qu.:1.205 1st Qu.:0.2700
## Median : 98.00 Median :2.355 Median :2.135 Median :0.3400
## Mean : 99.74 Mean :2.295 Mean :2.029 Mean :0.3619
## 3rd Qu.:107.00 3rd Qu.:2.800 3rd Qu.:2.875 3rd Qu.:0.4375
## Max. :162.00 Max. :3.880 Max. :5.080 Max. :0.6600
## Proanthocyanins Color_Intensity Hue OD280
## Min. :0.410 Min. : 1.280 Min. :0.4800 Min. :1.270
## 1st Qu.:1.250 1st Qu.: 3.220 1st Qu.:0.7825 1st Qu.:1.938
## Median :1.555 Median : 4.690 Median :0.9650 Median :2.780
## Mean :1.591 Mean : 5.058 Mean :0.9574 Mean :2.612
## 3rd Qu.:1.950 3rd Qu.: 6.200 3rd Qu.:1.1200 3rd Qu.:3.170
## Max. :3.580 Max. :13.000 Max. :1.7100 Max. :4.000
## Proline
## Min. : 278.0
## 1st Qu.: 500.5
## Median : 673.5
## Mean : 746.9
## 3rd Qu.: 985.0
## Max. :1680.0Scale data for K-means, subtracts mean and divide by standard divation on each column
wine_data_scale <- scale(wine_data)Distances - distance between rows of dataframe
wine_distance <- dist(wine_data_scale)Calculate clusters we need, figure out K, use within sum square
Elbow occurs at k=3
fviz_nbclust(wine_data_scale, kmeans, method='wss') +
labs(subtitle = 'elbow method')
kmeans cluster
wine_kmeans <- kmeans(wine_data_scale, centers=3, nstart = 100)
print(wine_kmeans)## K-means clustering with 3 clusters of sizes 51, 62, 65
##
## Cluster means:
## Alcohol Malic_Acid Ash Ash_Alcanity Magnesium Total_Phenols
## 1 0.1644436 0.8690954 0.1863726 0.5228924 -0.07526047 -0.97657548
## 2 0.8328826 -0.3029551 0.3636801 -0.6084749 0.57596208 0.88274724
## 3 -0.9234669 -0.3929331 -0.4931257 0.1701220 -0.49032869 -0.07576891
## Flavanoids Nonflavanoid_Phenols Proanthocyanins Color_Intensity Hue
## 1 -1.21182921 0.72402116 -0.77751312 0.9388902 -1.1615122
## 2 0.97506900 -0.56050853 0.57865427 0.1705823 0.4726504
## 3 0.02075402 -0.03343924 0.05810161 -0.8993770 0.4605046
## OD280 Proline
## 1 -1.2887761 -0.4059428
## 2 0.7770551 1.1220202
## 3 0.2700025 -0.7517257
##
## Clustering vector:
## [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 2 2 2 2 2 2 2 2 2 2 2
## [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 1 3 3 3 3 3 3 3 3 3 3 3 2
## [75] 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [112] 3 3 3 3 3 3 3 1 3 3 2 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 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
##
## Within cluster sum of squares by cluster:
## [1] 326.3537 385.6983 558.6971
## (between_SS / total_SS = 44.8 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"Visualize the results of k-means
wine_clusters <- wine_kmeans$cluster
fviz_cluster(list(data=wine_data_scale, cluster=wine_clusters))
Center of the clusters
wine_kmeans$centers## Alcohol Malic_Acid Ash Ash_Alcanity Magnesium Total_Phenols
## 1 0.1644436 0.8690954 0.1863726 0.5228924 -0.07526047 -0.97657548
## 2 0.8328826 -0.3029551 0.3636801 -0.6084749 0.57596208 0.88274724
## 3 -0.9234669 -0.3929331 -0.4931257 0.1701220 -0.49032869 -0.07576891
## Flavanoids Nonflavanoid_Phenols Proanthocyanins Color_Intensity Hue
## 1 -1.21182921 0.72402116 -0.77751312 0.9388902 -1.1615122
## 2 0.97506900 -0.56050853 0.57865427 0.1705823 0.4726504
## 3 0.02075402 -0.03343924 0.05810161 -0.8993770 0.4605046
## OD280 Proline
## 1 -1.2887761 -0.4059428
## 2 0.7770551 1.1220202
## 3 0.2700025 -0.7517257Cluster labels
wine_kmeans$cluster## [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 2 2 2 2 2 2 2 2 2 2 2
## [38] 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 1 3 3 3 3 3 3 3 3 3 3 3 2
## [75] 3 3 3 3 3 3 3 3 3 1 3 3 3 3 3 3 3 3 3 3 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
## [112] 3 3 3 3 3 3 3 1 3 3 2 3 3 3 3 3 3 3 3 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## [149] 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