K-Means Clustering Implementation
Implement the K-means clustering algorithm from scratch.
Task:
Implement the K-means clustering algorithm from scratch using only NumPy. Your implementation should include:
- A function to initialize centroids randomly
- A function to assign data points to the nearest centroid
- A function to update centroids based on assigned points
- A function to calculate inertia (sum of squared distances)
- The main K-means algorithm with convergence criteria
Use the provided synthetic dataset to test your implementation and visualize the clustering results.
Code Editor
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Output
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