Evaluation Metrics for Unsupervised Learning
Understand the evaluation metrics used in unsupervised learning, including silhouette score, Calinski-Harabasz index, and Davies-Bouldin index, with practical examples.
Cluster Stability and Robustness
Explore methods to assess the stability and robustness of clustering results. Learn how techniques like bootstrapping, consensus clustering, and cross-validation can ensure reliable and consistent cluster assignments in unsupervised learning.
Soft Clustering vs. Hard Clustering
Explore the differences between soft and hard clustering, their use cases, and how to determine which method to apply in different machine learning scenarios.
Clustering in High-Dimensional Spaces
Investigate the challenges and solutions for clustering in high-dimensional environments. Explore the curse of dimensionality, dimensionality reduction strategies, and algorithms specifically designed to operate effectively in high-dimensional spaces.
Clustering Ensemble Methods
Discover clustering ensemble methods that combine multiple clustering solutions to improve robustness and accuracy. Learn about consensus clustering, voting-based approaches, and strategies for integrating diverse clustering algorithms to achieve superior clustering performance.