Add K-Means Clustering interactive lesson with demo and quiz

[LMK89]

Summary

Added a comprehensive interactive educational lesson on K-Means Clustering algorithm, including visual demonstrations, step-by-step execution tracing, and self-assessment quiz.

Key Changes

• New lesson file: 2026-08-23-kmeans-clustering-1dba440e.html — a complete standalone HTML lesson covering:

  • K-Means algorithm definition and mathematical formulation
  • 4-state algorithm trace (initialization → assignment → update → convergence)
  • Interactive canvas demo allowing users to manually step through the algorithm with configurable K values (2, 3, 4, 6)
  • Elbow Method visualization for optimal K selection
  • Data preprocessing best practices (scaling, outlier handling)
  • Real-world applications and practical considerations
  • 5-question self-assessment quiz with immediate feedback

• Interactive features:

  • Canvas-based visualization of data points and cluster centroids
  • Step-by-step execution controls (next step, reset, change K)
  • Real-time inertia calculation and iteration counter
  • Animated elbow chart showing J values across different K values
  • Quiz with correct/incorrect answer feedback

• Updated README: Added entry for the new lesson in the lesson index table

Implementation Details

• Responsive design with light/dark mode support using CSS custom properties
• Pure JavaScript implementation of K-Means algorithm (no external ML libraries in the demo)
• Euclidean distance metric for centroid assignment
• Smooth animations and visual feedback for algorithm state transitions
• Educational ASCII diagrams explaining each algorithm phase
• Comprehensive comparison tables for data quality and algorithm states

https://claude.ai/code/session_017N6C1nu6tsSWpPYiRFXvgd