K-means clustering with Julia

Data Science with Julia

Julia Workshop

K-Means Clustering

K-means clustering is an unsupervised machine learning algorithm used to partition a dataset into a predefined number of clusters (K). It aims to group similar data points together by minimizing the distance between each data point and the centroid (center) of its assigned cluster.

In the context of Julia programming, the line using Clustering signifies the following:

  • Loading a Package: It’s a command that tells the Julia interpreter to load the Clustering package. This package provides a collection of functions and data structures specifically designed for performing various clustering algorithms.
  • Accessing Functions and Data Structures: Once loaded, the using Clustering statement allows your code to directly use the functions and data structures defined within the Clustering package without needing to specify the package name every time.

Example:

using Clustering

# ... your code using functions like kmeans(), assignments(), centers() ...

In this example, after using Clustering, you can directly call functions like kmeans() to perform k-means clustering, assignments() to get cluster assignments for data points, and centers() to retrieve the coordinates of cluster centers.

Key Points:

  • The using keyword is a fundamental part of Julia’s package management system.
  • Loading packages is essential for accessing their functionalities within your code.
  • The Clustering package is a valuable resource for implementing different clustering algorithms in Julia.
using Clustering

# Sample data
data = rand(2, 100)  # 100 data points in 2 dimensions

# Number of clusters
k = 3

# Perform k-means clustering
result = kmeans(data, k)

# Get cluster assignments
assignments = assignments(result)

# Get cluster centers
centers = centers(result)

# Visualize the results (optional)
using Plots
scatter(data[1,:], data[2,:], group=assignments, markersize=5, legend=false)
scatter!(centers[1,:], centers[2,:], markersize=10, color=:red)

Explanation:

  1. Import the Clustering package: This line imports the necessary functions for k-means clustering.
  2. Generate sample data: This creates a 2x100 matrix of random numbers, representing 100 data points in a 2-dimensional space.
  3. Specify the number of clusters: The variable k is set to 3, indicating that we want to group the data into 3 clusters.
  4. Perform k-means clustering: The kmeans() function from the Clustering package performs the k-means algorithm on the data.
  5. Get cluster assignments: The assignments() function retrieves the cluster assignment for each data point.
  6. Get cluster centers: The centers() function retrieves the coordinates of the cluster centers.
  7. Visualize the results (optional): This part uses the Plots package to create a scatter plot of the data points, color-coded by their cluster assignments. The cluster centers are also plotted as red markers.

This code provides a basic example of k-means clustering in Julia. You can modify the data, the number of clusters, and the visualization options to suit your specific needs.