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Machine Learning with R
Clustering is an unsupervised learning technique used to group similar data points together. R provides several packages and functions to perform clustering. In this tutorial, we'll explore some popular clustering methods: k-means, hierarchical, and DBSCAN.
K-means tries to partition data into k pre-defined distinct non-overlapping groups (clusters).
Example using kmeans function:
# Simulate some data
set.seed(123)
data <- rbind(matrix(rnorm(100), ncol=2),
matrix(rnorm(100, mean=3), ncol=2))
# Apply k-means clustering
clusters <- kmeans(data, centers=2)
# Plot
plot(data, col=clusters$cluster)
points(clusters$centers, col=1:2, pch=8, cex=2)
Hierarchical clustering builds a tree of clusters. You can visualize this tree using a dendrogram.
Example using hclust function:
# Compute distance matrix dist_matrix <- dist(data) # Hierarchical clustering h_cluster <- hclust(dist_matrix) # Plot plot(h_cluster)
To cut the tree into k clusters:
groups <- cutree(h_cluster, k=2)
DBSCAN groups together points that are close to each other based on a distance measure and a minimum number of points.
Example using the dbscan package:
First, install and load the necessary package:
install.packages("dbscan")
library(dbscan)
Then apply DBSCAN:
set.seed(123) db <- dbscan(data, eps=0.5, minPts=5) # Plot plot(data, col=db$cluster)
For k-means, the Elbow method is commonly used:
wss <- numeric(10)
for (k in 1:10) {
model <- kmeans(data, centers=k)
wss[k] <- model$tot.withinss
}
plot(1:10, wss, type="b", xlab="Number of Clusters", ylab="WSS")
The 'elbow' of the curve represents an optimal number of clusters (a balance between precision and computational cost).
For clustering, evaluation can be challenging, especially if true labels are not known. Silhouette analysis can provide insights into the distance between the resulting clusters. More distant clusters lead to better clusterings.
library(cluster) silhouette_score <- silhouette(groups, dist_matrix) plot(silhouette_score)
Clustering is a powerful tool in unsupervised machine learning. R provides various methods and packages to perform clustering. Always ensure that you're preprocessing your data (e.g., scaling) appropriately and evaluating your clustering results using appropriate metrics or visual methods.
How to Perform Clustering Analysis in R:
# Load a sample dataset
data(iris)
# Select features for clustering
features <- iris[, c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")]
# Standardize the features (if necessary)
standardized_features <- scale(features)
Unsupervised Learning for Clustering in R:
# Using unsupervised learning for clustering kmeans_model <- kmeans(standardized_features, centers = 3) clusters_kmeans <- kmeans_model$cluster
Popular Clustering Packages in R:
# Popular clustering packages library(cluster) library(factoextra) library(dbscan)
K-Means Clustering in R:
# Using k-means clustering kmeans_model <- kmeans(standardized_features, centers = 3) clusters_kmeans <- kmeans_model$cluster
Hierarchical Clustering in R:
# Using hierarchical clustering hierarchical_model <- hclust(dist(standardized_features)) clusters_hierarchical <- cutree(hierarchical_model, k = 3)
DBSCAN Clustering in R:
# Using DBSCAN clustering dbscan_model <- dbscan(standardized_features, eps = 0.5, minPts = 5) clusters_dbscan <- dbscan_model$cluster
Agglomerative Clustering in R:
# Using agglomerative clustering agglomerative_model <- agnes(standardized_features) clusters_agglomerative <- cutree(agglomerative_model, k = 3)
Comparing Clustering Methods in R:
# Comparing clustering methods fviz_nbclust(standardized_features, kmeans, method = "silhouette")
Visualizing Clustering Results in R:
# Visualizing clustering results fviz_cluster(list(data = standardized_features, cluster = clusters_kmeans))