Clustering 1: monothetic vs. polythetic
Full lecture: http://bit.ly/K-means The aim of clustering is to partition a population into sub-groups (clusters). Clusters can be monothetic (where all cluster members share some common property) or polythetic (where all cluster members are similar to each other in some sense).
From playlist K-means Clustering
Clustering (3): K-Means Clustering
The K-Means clustering algorithm. Includes derivation as coordinate descent on a squared error cost function, some initialization techniques, and using a complexity penalty to determine the number of clusters.
From playlist cs273a
Clustering 3: overview of methods
Full lecture: http://bit.ly/K-means In this course we cover 4 different clustering algorithms: K-D trees (part of lecture 9), K-means (this lecture), Gaussian mixture models (lecture 17) and agglomerative clustering (lecture 20).
From playlist K-means Clustering
From playlist Hierarchical Clustering
Subspace and Network Averaging for Computer Vision and Bioinformatics -- Math Major Seminar
โญSupport the channelโญ Patreon: https://www.patreon.com/michaelpennmath Merch: https://teespring.com/stores/michael-penn-math My amazon shop: https://www.amazon.com/shop/michaelpenn ๐ข Discord: https://discord.gg/Ta6PTGtKBm โญmy other channelsโญ Main Channel: https://www.youtube.
From playlist MathMajor Seminar
Nexus Trimester - Harry Lang (Johns Hopkins University)
Data Reduction for Clustering on Streams Harry Lang (Johns Hopkins University) March 08, 2016 Abstract: We explore clustering problems in the streaming sliding window model in both general metric spaces and Euclidean space. We present the first polylogarithmic space O(1)-approximation to
From playlist 2016-T1 - Nexus of Information and Computation Theory - CEB Trimester
Clustering 2: soft vs. hard clustering
Full lecture: http://bit.ly/K-means A hard clustering means we have non-overlapping clusters, where each instance belongs to one and only one cluster. In a soft clustering method, a single individual can belong to multiple clusters, often with a confidence (belief) associated with each cl
From playlist K-means Clustering
CSE 519 --- Lecture 20: Clustering (Fall 2021)
11/18/21
From playlist CSE519 --- Data Science Fundamentals (Fall 2021)
Hierarchical Clustering 5: summary
[http://bit.ly/s-link] Summary of the lecture.
From playlist Hierarchical Clustering
Adam Polak: Nearly-Tight and Oblivious Algorithms for Explainable Clustering
We study the problem of explainable clustering in the setting first formalized by Dasgupta, Frost, Moshkovitz, and Rashtchian (ICML 2020). A k-clustering is said to b e explainable if it is given by a decision tree where each internal no de splits data points with a threshold cut in a sing
From playlist Workshop: Approximation and Relaxation
Introduction to Outlier Detection Methods - Wolfram Livecoding Session
Andreas Lauschke, a senior mathematical programmer, live-demos key Wolfram Language features useful in data science. In the sixth session, Andreas introduces some methods for outlier detection. This is part 1 of 2. A close look will be taken at box plots as well as caveats (i.e. when not t
From playlist Data Science with Andreas Lauschke
TabPy Tutorial For Beginners | TabPy Training | Tableau Training | Edureka | Tableau Rewind
๐ฅ๐๐๐ฎ๐ซ๐๐ค๐ ๐๐๐๐ฅ๐๐๐ฎ ๐๐๐ซ๐ญ๐ข๐๐ข๐๐๐ญ๐ข๐จ๐ง ๐๐ซ๐๐ข๐ง๐ข๐ง๐ : https://www.edureka.co/tableau-certification-training (๐๐ฌ๐ ๐๐จ๐๐: ๐๐๐๐๐๐๐๐๐) This Edureka tutorial on "TabPy Tutorial For Beginners " is to help you utilize donut charts as a tool, not only for engagement but also comprehension efficiency. Topic
From playlist Tableau Training Videos | Tableau Tutorial Videos | Data Visualisation using Tableau | Edureka
Clustering 5: The K-means algorithm
From playlist Clustering Algorithms
Practical, Fast, Beyond 2-pt Statistics for Large Scale Structure Clustering by Thomas Abel
PROGRAM LESS TRAVELLED PATH TO THE DARK UNIVERSE ORGANIZERS: Arka Banerjee (IISER Pune), Subinoy Das (IIA, Bangalore), Koushik Dutta (IISER, Kolkata), Raghavan Rangarajan (Ahmedabad University) and Vikram Rentala (IIT Bombay) DATE & TIME: 13 March 2023 to 24 March 2023 VENUE: Ramanujan
From playlist LESS TRAVELLED PATH TO THE DARK UNIVERSE
Stanford Seminar - Decision Making at Scale: Algorithms, Mechanisms, and Platforms
Ashish Goel Stanford University This seminar series features dynamic professionals sharing their industry experience and cutting edge research within the human-computer interaction (HCI) field. Each week, a unique collection of technologists, artists, designers, and activists will discuss
From playlist Stanford Seminars
CSE 519 -- Lecture 23, Fall 2020
From playlist CSE 519 -- Fall 2020
(ML 16.1) K-means clustering (part 1)
Introduction to the K-means algorithm for clustering.
From playlist Machine Learning