Clustering criteria

Elbow method (clustering)

In cluster analysis, the elbow method is a heuristic used in determining the number of clusters in a data set. The method consists of plotting the explained variation as a function of the number of clusters and picking the elbow of the curve as the number of clusters to use. The same method can be used to choose the number of parameters in other data-driven models, such as the number of principal components to describe a data set. The method can be traced to speculation by Robert L. Thorndike in 1953. (Wikipedia).

Elbow method (clustering)
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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

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In this tutorial, we shift gears and introduce the concept of clustering. Clustering is form of unsupervised machine learning, where the machine automatically determines the grouping for data. There are two major forms of clustering: Flat and Hierarchical. Flat clustering allows the scient

From playlist Machine Learning with Python

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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

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http://www.teachastronomy.com/ Galaxies are not scattered randomly in space. Gravity acting over ten billion years or more has caused them to move together. Sometimes two or more galaxies become bound by gravity and form a new entity. Astronomers have developed different mathematical te

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From playlist School on Cluster Algebras 2018

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Lecture 0805 Choosing the number of clusters

Machine Learning by Andrew Ng [Coursera] 08-01 Clustering

From playlist Machine Learning by Professor Andrew Ng

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Machine Learning Tutorial Python - 13: K Means Clustering Algorithm

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From playlist Introduction to Machine Learning

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Lecture 08-01 Clustering

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From playlist Machine Learning by Professor Andrew Ng

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From playlist Unsupervised Learning

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Clustering (4): Gaussian Mixture Models and EM

Gaussian mixture models for clustering, including the Expectation Maximization (EM) algorithm for learning their parameters.

From playlist cs273a

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From playlist Webinars by Edureka!

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From playlist 🔥Machine Learning | Machine Learning Tutorial For Beginners | Machine Learning Projects | Simplilearn | Updated Machine Learning Playlist 2023

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Hierarchical Clustering 4: the Lance-Williams algorithm

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From playlist ML Algorithms from Scratch

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From playlist Introduction to Algorithms

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Related pages

Variance | Knee of a curve | Mathematical optimization | Explained variation | Under-fitting | Scree plot | Determining the number of clusters in a data set | Cluster analysis