Optimizing-K-in-K-means-A-Visual-and-Quantitative-Exploration Cyberoctane29 · COMPLETED
Exploring K-means clustering through image color compression and high-dimensional data analysis. Learn how pixel grouping in RGB space builds intuition, while inertia/silhouette scores optimize clusters. Demonstrates K-means' power to reveal patterns in both visual and abstract data by optimizing groupings and selecting ideal k-values.
github.com/Cyberoctane29/Optimizing-K-in-K-means-A-Visual-and-Quantitative-Exploration · ★ 0 · Forks 0 · Size 17.5 MB
SUMMARY
Technologies 1
Scored 1
Observed 1
Practices 6
Evidence 1
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COVERAGE
Analyzed 6 files · 10 commits · 0 API calls
TECHNOLOGIES & DEPTH
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1 files · PRODUCTION
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ACTIVITY & OWNERSHIP
First commit 2025-05-14
Last commit 2025-05-17
Active months 1
Commits 10