Mechanics of K-means Clustering

Requirements Clarification & Assessment

• Objective: Understand the mechanics of K-means clustering, a popular unsupervised machine learning algorithm.
• Key Concepts:
  • Definition of K-means clustering
  • Initialization of centroids
  • Assignment of data points to clusters
  • Recalculation of centroids
  • Iterative process until convergence
  • Selection of the optimal number of clusters (k)
• Assumptions:
  • Familiarity with basic statistical concepts like mean and Euclidean distance
  • Understanding of unsupervised learning principles
  • Knowledge of iterative algorithm processes
• Expected Outcome: A comprehensive explanation that covers each step of the K-means algorithm, including its iterative nature and how the optimal number of clusters can be determined.