k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells. k-means clustering minimizes within-cluster variances (squared Euclidean distances), but not regular Euclidean distances, which would be the more difficult Weber problem: the mean optimizes squared errors, whereas only the geometric median minimizes Euclidean distances. For instance, better Euclidean solutions can be found using k-medians and k-medoids. The problem is computationally difficult (NP-hard); however, efficient heuristic algorithms converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both k-means and Gaussian mixture modeling. They both use cluster centers to model the data; however, k-means clustering tends to find clusters of comparable spatial extent, while the Gaussian mixture model allows clusters to have different shapes. The unsupervised k-means algorithm has a loose relationship to the k-nearest neighbor classifier, a popular supervised machine learning technique for classification that is often confused with k-means due to the name. Applying the 1-nearest neighbor classifier to the cluster centers obtained by k-means classifies new data into the existing clusters. This is known as nearest centroid classifier or Rocchio algorithm. (Wikipedia).
(ML 16.1) K-means clustering (part 1)
Introduction to the K-means algorithm for clustering.
From playlist Machine Learning
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
Clustering 7: intrinsic vs. extrinsic evaluation
Full lecture: http://bit.ly/K-means Clustering can be evaluated intrinsically (is it good in and of itself) or extrinsically (does it help you solve another problem).
From playlist K-means Clustering
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
Clustering 5: K-means objective and convergence
Full lecture: http://bit.ly/K-means K-means algorithm attempts to minimize the intra-cluster variance (aggregate distance from the cluster centroid to the instances in the cluster). K-means converges to a local minimum, so different initializations will result in different clusterings. K-
From playlist K-means Clustering
K-means clustering: how it works
Full lecture: http://bit.ly/K-means The K-means algorithm starts by placing K points (centroids) at random locations in space. We then perform the following steps iteratively: (1) for each instance, we assign it to a cluster with the nearest centroid, and (2) we move each centroid to the
From playlist K-means Clustering
Machine Learning by Andrew Ng [Coursera] 0801 Unsupervised learning introduction 0802 K-means algorithm 0803 Optimization objective 0804 Random initialization 0805 Choosing the number of clusters
From playlist Machine Learning by Professor Andrew Ng
K Means Clustering Algorithm | K Means Example in Python | Machine Learning Algorithms | Simplilearn
K Means Clustering Algorithm tutorial video byb siomplilearn focuses on helping the aspiring machine learning enthusiats to have the fundamental knowledge if all the machine learning algorithms along with K Means Clustering Algorithm. This Machine learning tutorial focuses on K Means Clust
K Means Clustering Algorithm | K Means In Python | Machine Learning Algorithms |Simplilearn
🔥 Enroll for FREE Machine Learning Course & Get your Completion Certificate: https://www.simplilearn.com/learn-machine-learning-basics-skillup?utm_campaign=MachineLearning&utm_medium=Description&utm_source=youtube This K Means clustering algorithm tutorial video will take you through mac
From playlist Machine Learning with Python | Complete Machine Learning Tutorial | Simplilearn [2022 Updated]
Lecture 0802 K-means algorithm
Machine Learning by Andrew Ng [Coursera] 08-01 Clustering
From playlist Machine Learning by Professor Andrew Ng
Statistical Learning: 12.3 k means Clustering
Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion: https://www.edx.org/course/statistical-learning
From playlist Statistical Learning
K-means clustering is used in all kinds of situations and it's crazy simple. The R code is on the StatQuest GitHub: https://github.com/StatQuest/k_means_clustering_demo/blob/master/k_means_clustering_demo.R For a complete index of all the StatQuest videos, check out: https://statquest.org
From playlist StatQuest
Applied ML 2020 - 14 - Clustering and Mixture Models
Course materials at https://www.cs.columbia.edu/~amueller/comsw4995s20/schedule/
From playlist Applied Machine Learning 2020
Clustering 6: how many clusters?
Full lecture: http://bit.ly/K-means How many clusters do we have in our data? The question turns out to be very tricky. We discuss using extrinsic factors (domain knowledge), intra-cluster distance, minimum description length (MDL) and methods based on the scree plot.
From playlist K-means Clustering