https://github.com/dlab-berkeley/Unsupervised-Learning-in-R

Workshop (6 hours): Clustering (Hdbscan, LCA, Hopach), dimension reduction (UMAP, GLRM), and anomaly detection (isolation forests).
https://github.com/dlab-berkeley/Unsupervised-Learning-in-R

clustering dimensionality-reduction glrm hdbscan isolation-forests latent-class-analysis umap unsupervised-learning

Last synced: about 2 months ago
JSON representation

Workshop (6 hours): Clustering (Hdbscan, LCA, Hopach), dimension reduction (UMAP, GLRM), and anomaly detection (isolation forests).

• Host: GitHub
• URL: https://github.com/dlab-berkeley/Unsupervised-Learning-in-R
• Owner: dlab-berkeley
• License: other
• Created: 2020-01-10T18:20:34.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2020-06-08T16:45:20.000Z (over 4 years ago)
• Last Synced: 2024-07-28T09:34:05.779Z (about 2 months ago)
• Topics: clustering, dimensionality-reduction, glrm, hdbscan, isolation-forests, latent-class-analysis, umap, unsupervised-learning
• Language: R
• Homepage: https://dlab-berkeley.github.io/Unsupervised-Learning-in-R/slides.html
• Size: 472 KB
• Stars: 45
• Watchers: 10
• Forks: 12
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
# Unsupervised Learning in R

Unsupervised machine learning is a class of algorithms that identifies patterns

in unlabeled data, i.e. without considering an outcome or target. This workshop

will describe and demonstrate powerful unsupervised learning algorithms used for

**clustering** (hdbscan, latent class analysis, hopach), **dimensionality

reduction** (umap, generalized low-rank models), and **anomaly detection** (isolation forests).

Participants will learn how to structure unsupervised

learning analyses and will gain familiarity with example code that can be

adapted to their own projects.

**Author**: [Chris Kennedy](http://github.com/ck37)

## Prerequisites

This is an intermediate machine learning workshop. Participants should have

significant prior experience with R and RStudio, including manipulation of data

frames, installation of packages, and plotting.

**Prerequisite workshops**

 * [R Fundamentals](https://github.com/dlab-berkeley/R-Fundamentals) or similar training in R basics.

 

**Recommended workshops**

 * [Machine Learning in R](https://github.com/dlab-berkeley/Machine-Learning-in-R) or other supervised learning experience.

## Technology requirements

Participants should have access to a computer with the following software:

 * [R version 3.6](https://cran.rstudio.com/) or greater

 * [RStudio](https://rstudio.com/products/rstudio/download/#download)

 * [RTools](https://cran.r-project.org/bin/windows/Rtools/) - if using Windows

 

## Initial steps for participants

To prepare for the workshop, please download the materials and work through the package installation in `0-install.Rmd`. Please report any errors to the [GitHub issue queue](https://github.com/dlab-berkeley/Unsupervised-Learning-in-R/issues).

There is also an [RStudio Cloud workspace](https://rstudio.cloud/project/930459) that can be used.

 

## Reporting errors or giving feedback

Please [create a GitHub issue](https://github.com/dlab-berkeley/Unsupervised-Learning-in-R/issues) to report any errors or give feedback on this workshop.

## Resources

Books

 * Boemke & Greenwell (2019). [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/) - free online version

 * Hennig et al. (2015). [Handbook of Cluster Analysis](https://smile.amazon.com/Handbook-Cluster-Analysis-Handbooks-Statistical-ebook/dp/B019FNKOJ4) - thorough and highly recommended

 * Aggarwal & Reddy. (2014). [Data clustering: algorithms and applications](https://smile.amazon.com/Data-Clustering-Algorithms-Applications-Knowledge-ebook/dp/B00EYROAQU/) - great complement to Hennig et al.

 * Dolnicar et al. (2018). [Market segmentation analysis](https://smile.amazon.com/Market-Segmentation-Analysis-Understanding-Professionals-ebook/dp/B07FQDSF3X/) - free, closely tied to R, and chapter 7 is especially helpful

 * Izenman (2013). [Modern Multivariate Statistical Techniques](https://www.amazon.com/Modern-Multivariate-Statistical-Techniques-Classification-ebook/dp/B00HWUR9CS/)

 * Everitt et al. (2011). [Cluster Analysis](https://www.amazon.com/Cluster-Analysis-Wiley-Probability-Statistics-ebook/dp/B005CPJSME)