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K-Means Clustering Machine Learning Visualizer

STEM Interactive Visual Learning Program at TEC-Bridge AI

Setup Data

Clustering Controls

Visualization

Colored dots: Data points, Stars: Centroids

Cluster Information

Algorithm Steps

How to Use

  1. Setup: Enter x,y data pairs or click "Sample Data"
  2. K Value: Set the number of clusters (2-8)
  3. Start: Click "Start" to begin K-means clustering
  4. Step Through: Click "Next" to see each iteration or "Run Through" for automatic execution
  5. Observe: Watch centroids move and clusters form
  6. Reset: Click "Reset" to start over

K-Means Clustering

K-Means Clustering partitions data into k clusters by minimizing within-cluster sum of squares.

How it works:

  • Initialize k centroids randomly
  • Assign each point to nearest centroid
  • Update centroids to cluster means
  • Repeat until convergence
  • Distance metric: Euclidean distance
  • Minimizes intra-cluster variance

Time Complexity: O(n×k×i) where n=points, k=clusters, i=iterations

K-Means Clustering Code Implementation

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