Lecture

Clustering: Unsupervised Learning

Description

This lecture covers the clustering problem, where points are grouped into clusters based on distance measures in high-dimensional space. It discusses the characteristics, methods, and examples of clustering, including hierarchical clustering and K-means. The instructor explains the challenges of clustering in high-dimensional spaces and provides insights into the K-means algorithm, its properties, drawbacks, and ways to determine the optimal number of clusters. Additionally, density-based clustering methods like DBSCAN are introduced, highlighting their advantages over centroid-based methods. The lecture concludes with a detailed explanation of DBSCAN algorithm and its performance.

About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.