K-means Clustering (Lloyd's algorithm)

[@Dexter4o4]

🎯 Key Takeaways for quick navigation: 00:12 🤖 *Lloyd's Algorithm (K-means)* - The algorithm, also known as Lloyd's algorithm or K-means, addresses the K-means problem, aiming to find partitions in an iterative and heuristic manner. 01:57 🔄 *Algorithm Steps* - The algorithm consists of initialization, computing means, and a reassignment step. It iteratively refines partitions based on distances between data points and cluster means. 06:50 🚦 *Convergence Criteria* - Convergence occurs when no data point desires to switch clusters, implying that each point is content with its assigned cluster, and no further improvement is possible. 09:18 ⏳ *Convergence and Optimality* - Lloyd's algorithm converges, providing reasonable clusters in practice. However, the converged solution may not necessarily be optimal for the original NP HARD problem. 10:54 ❓ *Key Questions* - Important questions include convergence confirmation, characteristics of produced clusters, the impact of initialization, and determining the suitable number of clusters (K) for a dataset.

[@vatg2001]

Hello 🙋‍♂️ everyone

[@Fahad-gf1wx]

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