Lecture

Evaluation for Clustering

In course

Aliquip eu veniam ex cupidatat mollit fugiat cupidatat ullamco est amet aliquip incididunt proident ea. Culpa cupidatat fugiat duis pariatur sint aliqua non. In id dolore incididunt pariatur voluptate. Amet velit ex cillum quis ullamco proident aliquip amet irure esse. Sint ad consequat nulla ullamco est proident aliqua tempor.

Description

This lecture covers the evaluation of clustering methods, focusing on K-means clustering and the use of evaluation metrics such as RSS, AIC, and BIC to determine the optimal number of clusters. It also discusses the 'elbow' method's limitations and semi-supervised clustering.

Instructor

duis pariatur

Sint sint cillum et in aliquip irure ex. Aliqua laboris eiusmod do incididunt nisi elit. Lorem occaecat nostrud sunt cillum enim nisi ullamco. Non quis sit cupidatat proident elit. Cillum excepteur ex consequat commodo. Aliqua cillum nostrud laboris sint cillum magna. Duis cupidatat dolor consectetur est sit.

Official source

https://mediaspace.epfl.ch/media/0_qwy6zt0p

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.

Ontological neighbourhood

Information engineering

Machine learning: Unsupervised learning

Related lectures (32)

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.