Unsupervised Learning Techniques
Unsupervised learning is a type of machine learning that deals with data without labeled responses. The goal is to uncover hidden structures in the data. In this topic, we will explore various unsupervised learning techniques, including clustering, dimensionality reduction, and association rule learning, with practical examples in R.
1. What is Unsupervised Learning?
Unsupervised learning algorithms analyze and cluster unlabeled datasets to find patterns or groupings without prior training on labeled data. It is particularly useful in exploratory data analysis.2. Key Techniques in Unsupervised Learning
2.1 Clustering
Clustering is the process of grouping a set of objects in such a way that objects in the same group (or cluster) are more similar to each other than to those in other groups.Example: K-Means Clustering
K-means is one of the most popular clustering algorithms. It partitions the data into K distinct clusters based on distance to the centroid of each cluster.R Example:
`R
Load necessary library
library(ggplot2)Create a sample dataset
set.seed(123) data <- data.frame(x = rnorm(100), y = rnorm(100))K-means clustering
set.seed(42) clusters <- kmeans(data, centers = 3) data$cluster <- as.factor(clusters$cluster)Plot the clusters
ggplot(data, aes(x = x, y = y, color = cluster)) + geom_point() + ggtitle('K-Means Clustering')`2.2 Dimensionality Reduction
Dimensionality reduction techniques reduce the number of features or variables in a dataset while preserving its essential characteristics. This is particularly useful in visualizing high-dimensional data.Example: Principal Component Analysis (PCA)
PCA transforms the data to a new coordinate system where the greatest variance by any projection lies on the first coordinate (principal component).R Example:
`R
Load necessary library
library(ggplot2)Create a sample dataset
iris_data <- iris[, -5]Exclude species information
Perform PCA
pca_result <- prcomp(iris_data, center = TRUE, scale. = TRUE)Visualize PCA results
biplot(pca_result)`2.3 Association Rule Learning
Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is often used in market basket analysis.Example: Apriori Algorithm
The Apriori algorithm identifies frequent itemsets in transactional data and derives association rules.R Example:
`R