Concept

Open cluster

Related lectures (32)

Explores galaxy cluster components, historical discoveries, X-ray and radio emissions, and gravitational lensing effects.

Compares K-Means and Spectral Clustering algorithms, highlighting their differences and practical applications in clustering student behaviors.

Explores properties of clusters and colorability threshold in graph coloring, including average connectivity and rigidity.

Covers the theory and applications of graph coloring, focusing on disassortative stochastic block models and planted coloring.

Explores molecular complex formation during cell adhesion using single-protein imaging techniques and DNA-PAINT simulations.

Covers the Leighton-Rao algorithm for finding the sparsest cut in a graph, focusing on its steps and theoretical foundations.

Covers stellar evolution, fusion processes, and the age of stellar clusters, emphasizing gas pressures and star evolution based on mass.

Covers the Stochastic Block Model for community detection, focusing on the detection of communities, clusters, and groups.

Explores the application of the Virial Theorem in astrophysics, focusing on nebulae mass analysis and the principle of maximum entropy.

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

Covers the setup of a Gitlab agent for Kubernetes, focusing on installation, version control, and troubleshooting.

Covers astronomical units, light years, parallax, exoplanets, stars, nebulae, and galaxy clusters.

Explores the history and principles of gravitational lensing, discussing strong and weak lensing, critical lines, and mass distribution effects.

Explores clustering in high-dimensional space, covering methods like hierarchical clustering, K-means, and DBSCAN.

Covers the K-means algorithm for clustering data samples into k classes without labels, aiming to minimize the loss function.

Covers the architectures for parallel databases, including Shared Memory, Shared Disk, and Shared Nothing.

Covers satisfiability threshold, clusters of solutions, parameter alpha, and average cluster computation.

Explores the evolution of brain region atlases through connectivity-based parcellation and cluster formation criteria.

Covers hybrid programming models, project proposals, thread safety, topology problems, and performance expectations in MPI.