Maintained by: David J. Birnbaum (djbpitt@gmail.com) Last modified: 2021-12-27T23:22:35+0000
Hierarchical cluster analysis(http://www.econ.upf.edu/~michael/stanford/maeb7.pdf ). From STA254 Department of Statistics, Stanford University, Fall, 2008, Correspondence Analysis and Related Methods, Michael Greenacre, Universitat Pompeu Fabra, Barcelona. General introduction, cutting the tree, permutation tests for valiation.
Hierarchical Clustering Essentials - Unsupervised Machine Learning(http://www.sthda.com/english/wiki/hierarchical-clustering-essentials-unsupervised-machine-learning). Part of Statistical tools for high-throughput data analysis (STHDA), http://www.sthda.com/english/. Clear, thorough, detailed, and accessible introductory tutorial. Table of contents: 1 Required R packages; 2 Algorithm; 3 Data preparation and descriptive statistics; 4 R functions for hierarchical clustering; 4.1 hclust() function; 4.2 agnes() and diana() functions; 4.2.1 R code for computing agne;s 4.2.2 R code for computing diana; 5 Interpretation of the dendrogram; 6 Cut the dendrogram into different groups; 7 Hierarchical clustering and correlation based distance; 8 What type of distance measures should we choose?; 9 Comparing two dendrograms; 9.1 Tanglegram; 9.2 Correlation matrix between a list of dendrogram. Not specifically about unsupervised machine learning.
Hierarchical Clustering with R (feat. D3.js and Shiny)(http://www.joyofdata.de/blog/hierarchical-clustering-with-r/). From Raffael Vogler’s Joy of data site. General introduction, cutting the tree. Not especially about Shiny or D3.
Introduction to dendextend(https://cran.r-project.org/web/packages/dendextend/vignettes/introduction.html). By Tal Galili.
The dendextend package offers a set of functions for extending dendrogram objects in R, letting you visualize and compare trees of hierarchical clusterings, you can: 1) Adjust a tree’s graphical parameters - the color, size, type, etc of its branches, nodes and labels. 2) Visually and statistically compare different dendrograms to one another.
Hierarchical cluster analysis on famous data sets - enhanced with the dendextend package(https://cran.r-project.org/web/packages/dendextend/vignettes/Cluster_Analysis.html). Good examples with clear explanations.
FAQ.(https://cran.r-project.org/web/packages/dendextend/vignettes/FAQ.html). Cookbook-type questions about dendrogram visualization.
Visualizing Dendrograms in R(https://rpubs.com/gaston/dendrograms). By Gaston Sanchez. Focus on decorating dendrograms (which can make them easier to understand) and on alternatives to dendrograms (most of which don’t ultimately seem to offer any advantages over dendrograms in expressing information).
Stacked Trees: a New Hybrid Visualization Method(http://iihm.imag.fr/publs/2012/AVI12-StackedTrees-Bisson.pdf). By Gilles Bisson and Renaud Blanch. Similar enough to dendrograms to be easy to understand intuitively, but with greater information density, and therefore able to represent larger datasets in an accessible way. More detailed report in Gilles Bisson, Renaud Blanch.
Improving Visualization of Large Hierarchical Clustering.International Conference on Information Visualisation (IV), Jul 2012, Montpellier, France. IEEE Conference Publications, pp.220-228, 2012, <10.1109/IV.2012.45>. <hal-00740734> (https://hal.archives-ouvertes.fr/hal-00740734/document).