Lecture

K-Means Clustering: Basics and Applications

In course

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Description

This lecture covers the fundamentals of K-Means Clustering, a simple yet effective algorithm used for grouping data points. The instructor explains the process of grouping samples into clusters based on their distances to cluster centers. Various applications of K-Means Clustering are discussed, including its use in image segmentation and color image analysis. The lecture also addresses the challenges of working with heterogeneous data and provides solutions to improve clustering accuracy.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_wofg0bfg

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Ontological neighbourhood

Information engineering

Machine learning: Unsupervised learning

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