Implement one-cluster k-means
Company: Sealth
Role: Machine Learning Engineer
Category: Coding & Algorithms
Difficulty: easy
Interview Round: Onsite
Given n points in d-dimensional space, implement k-means clustering with squared Euclidean distance for the special case k = 1. Return the single centroid that minimizes the total within-cluster squared error over all points. The interviewer may also ask you to explain why this special case converges immediately without repeated reassignment steps.
Quick Answer: This question evaluates understanding of clustering algorithms and optimization of within-cluster squared error for the special case k = 1, along with the ability to implement a numerical solution in d-dimensional space.