Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions. Such high-dimensional spaces of data are often encountered in areas such as medicine, where DNA microarray technology can produce many measurements at once, and the clustering of text documents, where, if a word-frequency vector is used, the number of dimensions equals the size of the vocabulary. (Wikipedia).
We will look at the fundamental concept of clustering, different types of clustering methods and the weaknesses. Clustering is an unsupervised learning technique that consists of grouping data points and creating partitions based on similarity. The ultimate goal is to find groups of simila
From playlist Data Science in Minutes
Clustering (2): Hierarchical Agglomerative Clustering
Hierarchical agglomerative clustering, or linkage clustering. Procedure, complexity analysis, and cluster dissimilarity measures including single linkage, complete linkage, and others.
From playlist cs273a
Introduction to Hierarchical Clustering with College Scorecard Data
Clustering is an unsupervised machine learning technique where data need not be labeled. The goal of clustering is to find like-items such as similar customers, similar products, or similar students, just to name a few. Popular clustering algorithms include K-means and hierarchical cluster
From playlist Fundamentals of Machine Learning
From playlist Clustering Algorithms
Model-based clustering of high-dimensional data: Pitfalls & solutions - David Dunson
Virtual Workshop on Missing Data Challenges in Computation, Statistics and Applications Topic: Model-based clustering of high-dimensional data: Pitfalls & solutions Speaker: David Dunson Date: September 9, 2020 For more video please visit http://video.ias.edu
From playlist Mathematics
Dimension reduction: UMAP to densMAP JupyterLab w/ PyTorch SBERT visualization (SBERT 18)
UMAP is a general purpose manifold learning and dimension reduction algorithm, which includes densMAP to preserve local density of your data. Experience the implications of applying densMAP to sentence embedding with SBERT, given real time coding of embedding 4000 sentences with PyTorch i
From playlist SBERT: Python Code Sentence Transformers: a Bi-Encoder /Transformer model #sbert
Visualizing high-dimensional biological data with Clustergrammer-Widget in the Jupyter Notebook
Visualizing high-dimensional biological data with Clustergrammer-Widget in the Jupyter Notebook Nicolas Fernandez (Icahn School of Medicine at Mount Sinai) Biological data and other data collected from complex systems can have tens of thousands of variables that interact nonlinearly. Inte
From playlist JupyterCon in New York 2018
Hierarchical Modeling of High-dimensional Human Immuno-phenotypic Diversity by Saumyadipta Pyne
DISCUSSION MEETING : MATHEMATICAL AND STATISTICAL EXPLORATIONS IN DISEASE MODELLING AND PUBLIC HEALTH ORGANIZERS : Nagasuma Chandra, Martin Lopez-Garcia, Carmen Molina-Paris and Saumyadipta Pyne DATE & TIME : 01 July 2019 to 11 July 2019 VENUE : Madhava Lecture Hall, ICTS, Bangalore
From playlist Mathematical and statistical explorations in disease modelling and public health
Bayesian data interpretation with large scale cosmological (...) - Jasche - Workshop 2 - CEB T3 2018
Jens Jasche (Stockholm University) / 25.10.2018 Bayesian data interpretation with large scale cosmological models ---------------------------------- Vous pouvez nous rejoindre sur les réseaux sociaux pour suivre nos actualités. Facebook : https://www.facebook.com/InstitutHenriPoincare/
From playlist 2018 - T3 - Analytics, Inference, and Computation in Cosmology
90% of the world's data is unstructured. It is built by humans, for humans. That's great for human consumption, but it is *very* hard to organize when we begin dealing with the massive amounts of data abundant in today's information age. Organization is complicated because unstructured te
From playlist Recommended
Smita Krishnaswamy: "Manifold-Learning Yields Insights into Single Cell Data Analysis"
Computational Genomics Winter Institute 2018 "Manifold-Learning Yields Insights into Single Cell Data Analysis" Smita Krishnaswamy, Yale University Institute for Pure and Applied Mathematics, UCLA February 27, 2018 For more information: http://computationalgenomics.bioinformatics.ucla.e
From playlist Computational Genomics Winter Institute 2018
John Healy (5/3/21): Practical Clustering and Topological Data Analysis
I will give a topologically biased history of useful and popular clustering from a data science perspective with links to the language of topological data analysis. Another way to phrase that could be: useful topological data analysis from the perspective of a data science practitioner. Th
From playlist TDA: Tutte Institute & Western University - 2021
Clustering -- Does Theory Help?
Ravi Kannan, Microsoft Research India Simons Institute Open Lectures http://simons.berkeley.edu/events/openlectures2013-fall-4
From playlist Simons Institute Berkeley
07 Machine Learning: Clustering
The first lecture on inferential machine learning with clustering. We focus on k means clustering with some comments on other clustering methods. Follow along with the demonstration workflows in Python: o. DataFrames from Pandas: https://github.com/GeostatsGuy/PythonNumericalDemos/blob/
From playlist Machine Learning
