K-Means Clustering and Visualization

# File Name: k_means_clustering.R

# Author: Gerard King - www.gerardking.dev

# Title: K-Means Clustering and Visualization

# Description: This R program generates a synthetic dataset, performs k-means clustering to group

# the data points into clusters, and visualizes the clusters using a scatter plot. The program helps

# understand the concept of clustering and how data points are grouped based on similarity.

# Use Cases:

# - Segmenting customers or users based on behavior

# - Grouping similar data points for exploratory data analysis

# - Clustering data points for pattern recognition and anomaly detection

# Audience:

# - Data scientists and machine learning practitioners

# - Students learning unsupervised learning techniques

# - Analysts and researchers working on data segmentation or pattern recognition

# Blue Team Uses:

# - Grouping similar system behaviors or network traffic for anomaly detection

# - Clustering system logs to detect abnormal patterns or attack behaviors

# - Identifying similar incidents or security events to predict future threats

# Red Team Uses:

# - Identifying clusters of potential attack points or weaknesses in a system

# - Simulating clusters of attack patterns to evaluate security defenses

# - Detecting groups of vulnerabilities or exploits that share common characteristics

# Current Date: 2025-03-06

# Load necessary libraries

library(ggplot2)

# Set the seed for reproducibility

set.seed(202)

# Generate synthetic data with 2 features (e.g., x and y coordinates)

data <- data.frame(

  x = c(rnorm(50, mean = 3), rnorm(50, mean = 7)),

  y = c(rnorm(50, mean = 3), rnorm(50, mean = 7))

)

# Perform k-means clustering with 2 clusters (k = 2)

kmeans_result <- kmeans(data, centers = 2)

# Add cluster assignment to the dataset

data$cluster <- as.factor(kmeans_result$cluster)

# Create a scatter plot of the data points with different colors for each cluster

ggplot(data, aes(x = x, y = y, color = cluster)) +

  geom_point(size = 3) +

  labs(title = "K-Means Clustering Visualization", x = "Feature 1 (x)", y = "Feature 2 (y)") +

  scale_color_manual(values = c("red", "blue")) +

  theme_minimal()

# Display the cluster centers

cat("Cluster Centers:\n")

print(kmeans_result$centers)

# Print the current date for reference

cat("Date of execution:", Sys.Date(), "\n")