Concept

K-means clustering

Related lectures (32)

Explores advanced graph coloring concepts, including planted coloring, rigidity threshold, and frozen variables in BP fixed points.

Unsupervised Learning: Clustering & Dimensionality Reduction

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Kernel K-Means: Convergence Proof

Explores the Kernel K-Means algorithm, convergence proof, RBF kernel influence, and clustering interpretation.

Clustering Methods and Dimensionality Reduction

Covers clustering methods and dimensionality reduction techniques.

Kernel K-means Clustering

Explores Kernel K-means clustering, interpreting solutions, handling missing data, and dataset selection for machine learning.

FOCAL3D: Clustering Algorithm for SMLM

Introduces FOCAL3D, a fast clustering algorithm for SMLM, addressing parameter selection and run-time issues.

Cluster Analysis: Methods and Applications

Explores cluster analysis methods and applications in genomic data analysis, covering classification, gene expression clustering, visualization, distance metrics, and clustering algorithms.

Statistical Physics of Clusters

Explores the statistical physics of clusters, focusing on complexity and equilibrium behavior.

Kernel K-Means Method

Introduces the kernel k-means method to form non-convex clusters and discusses clustering by density to identify dense regions in datasets.

Evaluation for Clustering

Covers the evaluation of clustering methods, including K-means clustering and the use of evaluation metrics to determine the optimal number of clusters.

Single Proteins Maps: Sabrina Simoncelli

Explores the transition from single-molecule localizations to single proteins maps using DNA-PAINT and k-means clustering.

K-Means Clustering: Image Compression

Covers K-means algorithm for image compression and PCA for dimensionality reduction.

Unsupervised Learning: Clustering

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

K-Means Clustering: Basics and Applications

Introduces K-Means Clustering, a simple yet effective algorithm for grouping data points into clusters.

Supervised Learning: k-NN and Decision Trees

Introduces supervised learning with k-NN and decision trees, covering techniques, examples, and ensemble methods.

Clustering: k-means

Explains k-means clustering, assigning data points to clusters based on proximity and minimizing squared distances within clusters.

K-means and Gaussian Mixture Model

Introduces K-means clustering, the Gaussian mixture model, Jensen's inequality, and the EM algorithm.

K-means Clustering: Assignment and Update Steps

Explains the assignment and update steps in K-means clustering, loss function minimization, and distance metric effects.

Clustering: Principles and Methods

Covers the principles and methods of clustering in machine learning, including similarity measures, PCA projection, K-means, and initialization impact.

Support Vector Clustering: SVC

Introduces Support Vector Clustering (SVC) using a Gaussian kernel for high-dimensional feature space mapping and explains its constraints and Lagrangian.

Graph Chat

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.