Music Taste Clustering Game

Cluster fictional songs, tune feature weights, compare algorithms, build playlists, detect outliers, and discover how unsupervised learning reveals hidden listening patterns through interactive exploration.

Clustering scorecard

Improve separation, compactness, playlist quality, and challenge accuracy.

Total score0Points
Silhouette0.00Higher is better
Compactness0Lower distance wins
Separation0Centroid distance
Iterations0Training steps
Timer00:00Current round

Cluster canvas

Click two songs to compare. Drag songs in manual mode.
Tempo × Energy Explore mode Ready

Mission control

Create three distinct workout playlists

Use tempo, energy, and danceability. Reach a silhouette score above 0.40 with exactly three clusters.

Target: 3 clusters · Silhouette ≥ 0.40 · Tempo, energy, danceability enabled
Mission not checked.

Plotly.js dashboard

Cluster playlists

Fictional song records

SongGenreTempoEnergyMoodDurationPopularityCluster

Round analysis

Run clustering to generate an educational analysis.

Learning guide

1. Prepare the data

  • Select useful musical features.
  • Adjust weights to control influence.
  • Choose scaling and genre encoding.

2. Train and inspect

  • Run or step through clustering.
  • Inspect centroids, outliers, and metrics.
  • Compare algorithms and feature choices.

3. Build playlists

  • Name each discovered music group.
  • Move songs manually when appropriate.
  • Export playlists and learning reports.

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Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.