Clustering: K-Means

About
Privacy
Disclaimer

Graph Chatbot

In course

Elit amet ut aliqua ad id. Pariatur voluptate do non eiusmod do elit tempor dolor officia ullamco. Deserunt dolore mollit consectetur quis do labore. Proident quis minim commodo exercitation laborum ut tempor nulla.

Description

This lecture by the instructor covers the concepts of clustering and classification, focusing on the K-means algorithm. It explains how to partition a dataset into clusters based on similarity, the characteristics of clustering methods, the K-means algorithm, and its application for categorical attributes. The lecture also discusses how to choose the optimal number of clusters and the advantages and shortcomings of K-means.

Instructor

aute laborum

Mollit laboris deserunt laborum officia qui nisi elit enim ad non reprehenderit ad. Cupidatat tempor nulla enim Lorem ipsum est amet magna. Adipisicing aliquip tempor occaecat et est sint irure tempor elit elit.

Official source

Related lectures (32)

https://mediaspace.epfl.ch/media/0_zdtv017z

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.

In course

DEMO: dolor incididunt

Instructor

aute laborum

Related lectures (32)

Introduction to Image Classification

Covers image classification, clustering, and machine learning techniques like dimensionality reduction and reinforcement learning.

Unsupervised Learning: Clustering

Explores unsupervised learning through clustering techniques, algorithms, applications, and challenges in various fields.

Supervised Learning Overview

Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.

Clustering: Theory and Practice

Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.

Nearest Neighbor Rules: Part 2

Explores the Nearest Neighbor Rules, k-NN algorithm challenges, Bayes classifier, and k-means algorithm for clustering.