Key part of algorithm is convergence (the process by which cluster centers and point assignments gradually stabilize through repeated updates).
🟢 HOW & WHY convergence happens?
☞ Quickly converges on most datasets, making it efficient for large-scale tasks
☞ Offers a simple and interpretable structure for identifying groups
☞ Scales well on large datasets due to low computational complexity
☞ Results heavily depend on the initial cluster initialization
☞ Can distort data structure if features are improperly scaled
☞ May produce empty or unstable clusters if not properly configured
To ensure stable convergence:
- Use k-means++ for a more informed choice of initial centers
- Apply feature scaling so that variables with large scales do not dominate
- Set appropriate values for iteration limits and convergence thresholds
The image shows the K-means convergence process.
Data points are assigned to the nearest center based on squared distance. Then each center is recalculated as the mean of all points assigned to it.
These steps repeat until the positions of the centers no longer change significantly.
Understanding Convergence helps to obtain reliable and meaningful clustering results. #DataScience #MachineLearning #DS #ML
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