Lecture
Clustering Methods
Description
This lecture covers clustering methods, focusing on partitioning data points into meaningful classes when the labeling is unknown. It discusses the Swiss Fertility and Socioeconomic Indicators dataset, similarity-based clustering, dissimilarity measures, K-means algorithm, and hierarchical clustering. The instructor explains the bias-variance tradeoff, initialization issues in K-means, and different linkage clustering methods.
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