K-Means Clustering Implementation

Implement the K-Means clustering algorithm to partition a set of

N

points into

K

clusters. You are given: points: A list of

D

-dimensional coordinates (as tuples). k: The number of clusters to identify. initial_centroids: A list of

K

points representing the starting positions of the centroids. max_iterations: The maximum number of assignment/update cycles to perform. Your algorithm should follow these steps: Assignment: Assign each point to the cluster associated with the nearest centroid using squared Euclidean distance. Update: Recompute the centroid of each cluster by taking the arithmetic mean of all points assigned to that cluster. If a cluster has no assigned points, its centroid remains unchanged for that iteration. Convergence: If the centroids do not change between two consecutive iterations, terminate early. The final output should be a list of the

K

cluster centroids, with each coordinate rounded to exactly 4 decimal places.

PythonK-Means ClusteringEuclidean Distance

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