Before heading towards color clustering, the main thing to understand here is what are the different types of color spaces.

• Color Spaces

Now, as we have understood about different types of color spaces, let’s start with the main clustering algorithm. We will head towards the final implementation by observing patterns in each iterations.

Iteration 1

Clustering began with a fundamental approach to color clustering using the HSL (Hue, Saturation, Lightness) color space. This iteration established the basic framework for color analysis and grouping. The system used predefined color centroids and simple distance calculations to categorize colors into basic families.

Key Features:

• HSL color space for intuitive color understanding
• Simple distance calculations for color matching
• Static centroid system with basic color categories
• Direct RGB to HSL conversion pipeline
• Basic color family grouping

While this iteration provided a solid foundation, it revealed the complexities of human color perception and the limitations of simple mathematical approaches to color relationships.

Iteration 2

The second iteration marked a significant shift toward perceptual color matching. By implementing the DeltaE2000 color difference formula, we dramatically improved our ability to match colors in a way that aligns with human vision.

Key Improvements:

• DeltaE2000 implementation for accurate color difference calculation
• Hybrid approach combining precise matching with RGB fallback
• Expanded color reference system with multiple variations
• Improved handling of edge cases
• Better support for design system colors