Cluster analysis algorithms

Single-linkage clustering

In statistics, single-linkage clustering is one of several methods of hierarchical clustering. It is based on grouping clusters in bottom-up fashion (agglomerative clustering), at each step combining two clusters that contain the closest pair of elements not yet belonging to the same cluster as each other. A drawback of this method is that it tends to produce long thin clusters in which nearby elements of the same cluster have small distances, but elements at opposite ends of a cluster may be much farther from each other than two elements of other clusters. This may lead to difficulties in defining classes that could usefully subdivide the data. (Wikipedia).

Single-linkage clustering
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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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Hierarchical Clustering 3: single-link vs. complete-link

[http://bit.ly/s-link] Agglomerative clustering needs a mechanism for measuring the distance between two clusters, and we have many different ways of measuring such a distance. We explain the similarities and differences between single-link, complete-link, average-link, centroid method and

From playlist Hierarchical Clustering

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Applied topology 12: Hierarchical clustering and single-linkage clustering

Applied topology 12: Hierarchical clustering and single-linkage clustering Abstract: We describe hierarchical clustering and dendrograms. The particular hierarchical clustering technique we describe is the simplest one, single-linkage clustering. There are many other hierarchical clusteri

From playlist Applied Topology - Henry Adams - 2021

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Introduction to Clustering

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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Clustering Introduction - Practical Machine Learning Tutorial with Python p.34

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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Applied topology 13: The problem of chaining in single-linkage clustering

Applied topology 13: The problem of chaining in single-linkage clustering Abstract: We describe the chaining phenomenon, which is a downside of single-linkage clustering. Fancier hierarchical clustering techniques (such as average linkage) are often preferred over single-linkage because t

From playlist Applied Topology - Henry Adams - 2021

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Alexander Rolle (6/1/20): Stable and consistent density-based clustering

Title: Stable and consistent density-based clustering Abstract: We present a consistent approach to density-based clustering, which satisfies a stability theorem that holds without any distributional assumptions. We first define a 3-parameter hierarchical clustering of a metric probabilit

From playlist ATMCS/AATRN 2020

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Statistical Learning: 12.4 Hierarchical 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

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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

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Hierarchical Clustering | Agglomerative and Divisive Hierarchical Clustering Explained | Edureka

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12. Clustering

MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016 View the complete course: http://ocw.mit.edu/6-0002F16 Instructor: John Guttag Prof. Guttag discusses clustering. License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at

From playlist MIT 6.0002 Introduction to Computational Thinking and Data Science, Fall 2016

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Yulia Gel (4/28/21): Topological Clustering of Multilayer Networks

Title: Topological Clustering of Multilayer Networks Abstract: Multilayer networks continue to gain significant attention in many areas of study, particularly, due to their high utility in modeling interdependent systems such as critical infrastructures, human brain connectome, and socio-

From playlist AATRN 2021

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Related pages

Complete-linkage clustering | Ward's method | Dendrogram | Models of DNA evolution | Minimum spanning tree | Prim's algorithm | UPGMA | Statistics | WPGMA | Cluster analysis | Kruskal's algorithm | Hierarchical clustering