K-Means Clustering
K-Means Clustering is an unsupervised machine learning algorithm that partitions a dataset into K distinct, non-overlapping clusters by assigning each data point to its nearest centroid.
Frequently Asked Questions
How does K-Means assign points to clusters?▼
By calculating the Euclidean distance between points and centroids, iteratively updating centroids to minimize the inertia (squared distances).
What is a challenge of K-Means clustering?▼
Selecting the optimal value of K (often resolved using the Elbow Method) and its sensitivity to initial centroid locations.
Quick Facts
- CategoryFoundational AI
- Key ApplicationCustomer segmentation, image compression, and vector database clustering.
Coverage Trend12 Weeks
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