In statistics, k-medians clustering is a cluster analysis algorithm. It is a variation of k-means clustering where instead of calculating the mean for each cluster to determine its centroid, one instead calculates the median. This has the effect of minimizing error over all clusters with respect to the 1-norm distance metric, as opposed to the squared 2-norm distance metric (which k-means does.) This relates directly to the k-median problem with respect to the 1-norm, which is the problem of finding k centers such that the clusters formed by them are the most compact. Formally, given a set of data points x, the k centers ci are to be chosen so as to minimize the sum of the distances from each x to the nearest ci. The criterion function formulated in this way is sometimes a better criterion than that used in the k-means clustering algorithm, in which the sum of the squared distances is used. The sum of distances is widely used in applications such as the facility location problem. The proposed algorithm uses Lloyd-style iteration which alternates between an expectation (E) and maximization (M) step, making this an expectationโmaximization algorithm. In the E step, all objects are assigned to their nearest median. In the M step, the medians are recomputed by using the median in each single dimension. (Wikipedia).
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