Lecture

Clustering Methods: K-means and Density Clustering

In course

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Description

This lecture covers the k-means algorithm, introducing the k-means++ algorithm to improve initialization and avoid suboptimal solutions. It explains the kernel trick for non-convex clusters and the application of k-means with kernels. Additionally, it delves into clustering by density, highlighting the DBSCAN algorithm and the evaluation criteria for clustering methods.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_a586t1al

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Ontological neighbourhood

Information engineering

Machine learning: Unsupervised learning

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