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Description
History
Algorithms
Standard algorithm (naive k-means)
Initialization methods
Complexity
Variations
Hartigan–Wong method
Global optimization and meta-heuristics
Discussion
Applications
Vector quantization
Cluster analysis
Feature learning
Relation to other algorithms
Gaussian mixture model
k-SVD
Principal component analysis
Mean shift clustering
Independent component analysis
Bilateral filtering
Similar problems
Software implementations
Free Software/Open Source
Proprietary
See also
References
k-means clustering
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Tools
From Wikipedia, the free encyclopedia
Not to be confused with k-nearest neighbors algorithm.
Part of a series on
Machine learning
and data mining
Paradigms
Problems
Supervised learning
(classification • regression)
Clustering
Dimensionality reduction
Structured prediction
Anomaly detection
Artificial neural network
Reinforcement learning
Learning with humans
Model diagnostics
Mathematical foundations
Machine-learning venues
Related articles
vte
k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster.
KONKRET GRANSKNING. En vanlig missuppfattning är att migrationspolitiken har varit för generös i landet, eller att migrationen har varit ”okontrollerad”. Som framgår av offentligt tillgänglig statistik så har bara en liten del av alla uppehållstillstånd gått till flyktingar och asylsökande
Many people have flipped coins but few have stopped to ponder the statistical and physical intricacies of the process. In a preregistered study we collected 350,757 coin flips to test the counterintuitive prediction from a physics model of human coin tossing developed by Diaconis, Holmes, and Montgomery (D-H-M; 2007).
We introduce a mathematical framework for statistical exoplanet population and astrobiology studies that may help directing future observational efforts and experiments.
arXiv is a free distribution service and an open-access archive for 2,299,453 scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.
open-access archive for 2,273,366 scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics.
This book will teach you how to do data science with R: You’ll learn how to get your data into R, get it into the most useful structure, transform it, visualise it and model it. In this book, you will find a practicum of skills for data science. Just as a chemist learns how to clean test tubes and stock a lab, you’ll learn how to clean data and draw plots—and many other things besides. These are the skills that allow data science to happen, and here you will find the best practices for doing each of these things with R. You’ll learn how to use the grammar of graphics, literate programming, and reproducible research to save time. You’ll also learn how to manage cognitive resources to facilitate discoveries when wrangling, visualising, and exploring data.
El Objetivo del paquete aprendeR es facilitar que nuevas personas puedan R moderno con una curva de aprendizaje baja, y evitando que el inglés sea una barrera para que se puedan centrar en el aprendizaje competencial de R. Se incluyen traducciones al castellano de tutoriales presentes en otros paquetes (learnr, tutorial.helpers, r4ds.tutorials, ...), y eventualmente nuevos tutoriales más adelante.
D. Heurtel-Depeiges, B. Burkhart, R. Ohana, and B. Blancard. (2023)cite arxiv:2310.16285Comment: 5+6 pages, 2+3 figures, submitted to "Machine Learning and the Physical Sciences" NeurIPS Workshop.