Clustering:iris data with R

Using tidymodels

1 Introduction

This document demonstrates how to perform clustering in R using the tidymodels framework. Clustering is an unsupervised learning technique that groups similar data points together based on their inherent characteristics. We will use the iris dataset for this demonstration.

2 Load Data

First, we load the necessary libraries and the iris dataset.

Code

library(tidyverse)
library(tidymodels)
library(factoextra)

data(iris)
iris_data <- iris %>% select(-Species)

3 Elbow Method

The Elbow Method is a heuristic used to determine the optimal number of clusters in a dataset. We can visualize the total within-cluster sum of squares as a function of the number of clusters. 3 looks like a good number.

Code

fviz_nbclust(iris_data, kmeans, method = "wss") +
  labs(subtitle = "Elbow Method")

4 K-Means Clustering

K-Means is a popular clustering algorithm. We will use it to group the iris data into 3 clusters.

Code

set.seed(123)
kmeans_model <- kmeans(iris_data, centers = 3, nstart = 25)

# Visualize the clusters
fviz_cluster(kmeans_model, data = iris_data)

5 Hierarchical Clustering

Hierarchical clustering is another common clustering method.

Code

# Calculate the distance matrix
dist_matrix <- dist(iris_data, method = "euclidean")

# Perform hierarchical clustering
hclust_model <- hclust(dist_matrix, method = "ward.D2")

# Visualize the dendrogram
fviz_dend(hclust_model, k = 3, # Cut in 3 groups
          cex = 0.5, # label size
          k_colors = c("#2E9FDF", "#00AFBB", "#E7B800"),
          color_labels_by_k = TRUE, # color labels by groups
          rect = TRUE # Add rectangle around groups
          )

6 Comparison with Real Groups

We can compare the clustering results with the actual species of the iris flowers.

Code

# K-Means Comparison
iris$kmeans_cluster <- kmeans_model$cluster
print("K-Means Clustering vs. Real Species")

[1] "K-Means Clustering vs. Real Species"

Code

table(iris$kmeans_cluster, iris$Species)

   
    setosa versicolor virginica
  1      0         48        14
  2      0          2        36
  3     50          0         0

Code

# Hierarchical Clustering Comparison
iris$hclust_cluster <- cutree(hclust_model, k = 3)
print("Hierarchical Clustering vs. Real Species")

[1] "Hierarchical Clustering vs. Real Species"

Code

table(iris$hclust_cluster, iris$Species)

   
    setosa versicolor virginica
  1     50          0         0
  2      0         49        15
  3      0          1        35

7 Comparison of K-Means and Hierarchical Clustering

Here’s a comparison of K-Means and Hierarchical Clustering:

Feature	K-Means Clustering	Hierarchical Clustering
Approach	Partitioning (divides data into k clusters)	Agglomerative (bottom-up) or Divisive (top-down)
Number of Clusters	Requires pre-specification (k)	Does not require pre-specification; dendrogram helps
Computational Cost	Faster for large datasets	Slower for large datasets (O(n^3) or O(n^2))
Cluster Shape	Tends to form spherical clusters	Can discover arbitrarily shaped clusters
Sensitivity to Outliers	Sensitive to outliers	Less sensitive to outliers
Interpretability	Easy to interpret	Dendrogram can be complex for large datasets
Reproducibility	Can vary with initial centroids (unless fixed)	Reproducible

8 Conclusion

This document provided a brief overview of clustering in R using tidymodels. We demonstrated both K-Means and Hierarchical clustering on the iris dataset.

--- title: "Clustering:iris data with R" subtitle: "Using tidymodels" execute: warning: false error: false format: html: toc: true toc-location: right code-fold: show code-tools: true number-sections: true code-block-bg: true code-block-border-left: "#31BAE9" --- ## Introduction This document demonstrates how to perform clustering in R using the `tidymodels` framework. Clustering is an unsupervised learning technique that groups similar data points together based on their inherent characteristics. We will use the `iris` dataset for this demonstration. ## Load Data First, we load the necessary libraries and the `iris` dataset. ```{r} #| label: load-data #| echo: true library(tidyverse) library(tidymodels) library(factoextra) data(iris) iris_data <- iris %>% select(-Species) ``` ## Elbow Method The Elbow Method is a heuristic used to determine the optimal number of clusters in a dataset. We can visualize the total within-cluster sum of squares as a function of the number of clusters. 3 looks like a good number. ```{r} #| label: elbow-method #| echo: true fviz_nbclust(iris_data, kmeans, method = "wss") + labs(subtitle = "Elbow Method") ``` ## K-Means Clustering K-Means is a popular clustering algorithm. We will use it to group the iris data into 3 clusters. ```{r} #| label: kmeans #| echo: true set.seed(123) kmeans_model <- kmeans(iris_data, centers = 3, nstart = 25) # Visualize the clusters fviz_cluster(kmeans_model, data = iris_data) ``` ## Hierarchical Clustering Hierarchical clustering is another common clustering method. ```{r} #| label: hclust #| echo: true # Calculate the distance matrix dist_matrix <- dist(iris_data, method = "euclidean") # Perform hierarchical clustering hclust_model <- hclust(dist_matrix, method = "ward.D2") # Visualize the dendrogram fviz_dend(hclust_model, k = 3, # Cut in 3 groups cex = 0.5, # label size k_colors = c("#2E9FDF", "#00AFBB", "#E7B800"), color_labels_by_k = TRUE, # color labels by groups rect = TRUE # Add rectangle around groups ) ``` ## Comparison with Real Groups We can compare the clustering results with the actual species of the iris flowers. ```{r} #| label: comparison #| echo: true # K-Means Comparison iris$kmeans_cluster <- kmeans_model$cluster print("K-Means Clustering vs. Real Species") table(iris$kmeans_cluster, iris$Species) ``` ```{r} # Hierarchical Clustering Comparison iris$hclust_cluster <- cutree(hclust_model, k = 3) print("Hierarchical Clustering vs. Real Species") table(iris$hclust_cluster, iris$Species) ``` ## Comparison of K-Means and Hierarchical Clustering Here's a comparison of K-Means and Hierarchical Clustering: | Feature | K-Means Clustering | Hierarchical Clustering | |:--------------------|:-------------------------------------------------|:------------------------------------------------------| | **Approach** | Partitioning (divides data into k clusters) | Agglomerative (bottom-up) or Divisive (top-down) | | **Number of Clusters** | Requires pre-specification (k) | Does not require pre-specification; dendrogram helps | | **Computational Cost** | Faster for large datasets | Slower for large datasets (O(n^3) or O(n^2)) | | **Cluster Shape** | Tends to form spherical clusters | Can discover arbitrarily shaped clusters | | **Sensitivity to Outliers** | Sensitive to outliers | Less sensitive to outliers | | **Interpretability** | Easy to interpret | Dendrogram can be complex for large datasets | | **Reproducibility** | Can vary with initial centroids (unless fixed) | Reproducible | ## Conclusion This document provided a brief overview of clustering in R using `tidymodels`. We demonstrated both K-Means and Hierarchical clustering on the `iris` dataset.