Concept

Single-linkage clustering

Related lectures (32)

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

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 Methods

Covers K-means, hierarchical, and DBSCAN clustering methods with practical examples.

Clustering Methods and Dimensionality Reduction

Covers clustering methods and dimensionality reduction techniques.

Unsupervised Learning: Clustering

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

Introduction to Image Classification

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

Spin Glasses and Bayesian Estimation

Covers the concepts of spin glasses and Bayesian estimation, focusing on observing and inferring information from a system closely.

Unsupervised Learning: Clustering Methods

Explores unsupervised learning through clustering methods like K-means and DBSCAN, addressing challenges and applications.

Genomic Data Analysis: Clustering and Survival

Explores genomic data clustering, survival analysis, gene identification, and statistical significance in cancer research.

Clustering Methods

Explores clustering methods for partitioning data into meaningful classes when labeling is unknown, covering K-means, dissimilarity measures, and hierarchical clustering.

Hierarchical Clustering: Dendrograms and Linkage Functions

Explores hierarchical clustering, dendrograms, and various linkage functions for cluster agglomeration based on distance measures.

Clustering: Unsupervised Learning

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

Graph Coloring II

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

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.

Clustering: K-Means

Covers clustering and the K-means algorithm for partitioning datasets into clusters based on similarity.

Efficient Data Clustering

Covers efficient data exploitation through clustering methods and the optimization of market returns using asset clustering.

Supervised Learning: k-NN and Decision Trees

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

Clustering: Hierarchical and K-means Methods

Introduces hierarchical and k-means clustering methods, discussing construction approaches, linkage functions, Ward's method, the Lloyd algorithm, and k-means++.

Graph Coloring: Random vs Symmetrical

Compares random and symmetrical graph coloring in terms of cluster colorability and equilibrium.