K-Means Clustering
Clustering DS practice problem on Onlearn.
Difficulty: medium.
Topics: Understanding K-Means Clustering, Euclidean Distance, Voronoi Partitioning, Lloyd's Algorithm, Sum of Squared Errors, Centroid Update Rule, Unsupervised Learning, Linear Algebra, Computational Geometry, Optimization Theory, Data Structures, Centroid-based Clustering, Vector Space Operations, Iterative Refinement, Distance Metrics, Convergence Analysis.
Your task is to write a Python function that implements the k Means clustering algorithm. This function should take specific inputs and produce a list of final centroids. k Means clustering is a method used to partition n points into k clusters. The goal is to group similar points together and represent each group by its center (called the centroid ). Function Inputs: points: A list of points, where each point is a tuple of coordinates (e.g., (x, y) for 2D points) k: An integer representing the number of clusters to form initial centroids: A list of initial centroid points, each a tuple of coordinates max iterations: An integer representing the maximum number of iterations to perform Function Output: A list of the final centroids of the clusters, where each centroid is rounded to the nearest fourth decimal.