Introduction
Learn the fundamentals of K-Means Clustering, a popular unsupervised learning algorithm used to partition data into distinct clusters. This introduction covers the basic concepts, applications, and key advantages of K-Means Clustering.
Theory
Delve into the mathematical foundations of K-Means Clustering, exploring the algorithm's theoretical underpinnings, including the objective function, distance metrics, convergence properties, and complexities.
Scikit-learn Example
A practical guide to implementing K-Means Clustering using Scikit-learn, complete with code examples, parameter explanations, and tips for effective usage in real-world applications.
TensorFlow Example
Use TensorFlow tensors around a maintained K-Means implementation and understand the boundary between tensor operations and a clustering estimator.
PyTorch Example
Use PyTorch tensors around a maintained K-Means implementation and inspect the algorithm's tensor operations without mistaking them for a native estimator.
Common Mistakes and Best Practices
Avoid common K-Means mistakes involving geometry, scaling, initialization, cluster counts, outliers, and validation.
Comparison with Other Algorithms
A comprehensive comparison of K-Means Clustering with other popular unsupervised learning algorithms such as DBSCAN, Hierarchical Clustering, and more, highlighting the key differences, strengths, and best use cases.