{"id":13665800,"url":"https://github.com/dlab-berkeley/Unsupervised-Learning-in-R","last_synced_at":"2025-04-26T08:33:19.653Z","repository":{"id":61678923,"uuid":"233106092","full_name":"dlab-berkeley/Unsupervised-Learning-in-R","owner":"dlab-berkeley","description":"Workshop (6 hours): Clustering (Hdbscan, LCA, Hopach), dimension reduction (UMAP, GLRM), and anomaly detection (isolation forests).","archived":false,"fork":false,"pushed_at":"2020-06-08T16:45:20.000Z","size":483,"stargazers_count":47,"open_issues_count":0,"forks_count":12,"subscribers_count":10,"default_branch":"master","last_synced_at":"2024-11-11T00:37:09.535Z","etag":null,"topics":["clustering","dimensionality-reduction","glrm","hdbscan","isolation-forests","latent-class-analysis","umap","unsupervised-learning"],"latest_commit_sha":null,"homepage":"https://dlab-berkeley.github.io/Unsupervised-Learning-in-R/slides.html","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/dlab-berkeley.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-01-10T18:20:34.000Z","updated_at":"2024-08-30T07:17:57.000Z","dependencies_parsed_at":"2022-10-20T13:15:22.955Z","dependency_job_id":null,"html_url":"https://github.com/dlab-berkeley/Unsupervised-Learning-in-R","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dlab-berkeley%2FUnsupervised-Learning-in-R","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dlab-berkeley%2FUnsupervised-Learning-in-R/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dlab-berkeley%2FUnsupervised-Learning-in-R/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/dlab-berkeley%2FUnsupervised-Learning-in-R/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/dlab-berkeley","download_url":"https://codeload.github.com/dlab-berkeley/Unsupervised-Learning-in-R/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250960958,"owners_count":21514555,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["clustering","dimensionality-reduction","glrm","hdbscan","isolation-forests","latent-class-analysis","umap","unsupervised-learning"],"created_at":"2024-08-02T06:00:50.954Z","updated_at":"2025-04-26T08:33:14.643Z","avatar_url":"https://github.com/dlab-berkeley.png","language":"R","readme":"# Unsupervised Learning in R\n\n\nUnsupervised machine learning is a class of algorithms that identifies patterns\nin unlabeled data, i.e. without considering an outcome or target. This workshop\nwill describe and demonstrate powerful unsupervised learning algorithms used for\n**clustering** (hdbscan, latent class analysis, hopach), **dimensionality\nreduction** (umap, generalized low-rank models), and **anomaly detection** (isolation forests).\nParticipants will learn how to structure unsupervised\nlearning analyses and will gain familiarity with example code that can be\nadapted to their own projects.\n\n**Author**: [Chris Kennedy](http://github.com/ck37)\n\n## Prerequisites\n\nThis is an intermediate machine learning workshop. Participants should have\nsignificant prior experience with R and RStudio, including manipulation of data\nframes, installation of packages, and plotting.\n\n**Prerequisite workshops**\n\n * [R Fundamentals](https://github.com/dlab-berkeley/R-Fundamentals) or similar training in R basics.\n \n**Recommended workshops**\n\n * [Machine Learning in R](https://github.com/dlab-berkeley/Machine-Learning-in-R) or other supervised learning experience.\n\n## Technology requirements\n\nParticipants should have access to a computer with the following software:\n\n * [R version 3.6](https://cran.rstudio.com/) or greater\n * [RStudio](https://rstudio.com/products/rstudio/download/#download)\n * [RTools](https://cran.r-project.org/bin/windows/Rtools/) - if using Windows\n \n## Initial steps for participants\n\nTo 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).\n\nThere is also an [RStudio Cloud workspace](https://rstudio.cloud/project/930459) that can be used.\n \n## Reporting errors or giving feedback\n\nPlease [create a GitHub issue](https://github.com/dlab-berkeley/Unsupervised-Learning-in-R/issues) to report any errors or give feedback on this workshop.\n\n## Resources\n\nBooks\n\n * Boemke \u0026 Greenwell (2019). [Hands-on Machine Learning with R](https://bradleyboehmke.github.io/HOML/) - free online version\n * Hennig et al. (2015). [Handbook of Cluster Analysis](https://smile.amazon.com/Handbook-Cluster-Analysis-Handbooks-Statistical-ebook/dp/B019FNKOJ4) - thorough and highly recommended\n * Aggarwal \u0026 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.\n * 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\n * Izenman (2013). [Modern Multivariate Statistical Techniques](https://www.amazon.com/Modern-Multivariate-Statistical-Techniques-Classification-ebook/dp/B00HWUR9CS/)\n * Everitt et al. (2011). [Cluster Analysis](https://www.amazon.com/Cluster-Analysis-Wiley-Probability-Statistics-ebook/dp/B005CPJSME)\n","funding_links":[],"categories":["R"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdlab-berkeley%2FUnsupervised-Learning-in-R","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdlab-berkeley%2FUnsupervised-Learning-in-R","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdlab-berkeley%2FUnsupervised-Learning-in-R/lists"}