{"id":19702871,"url":"https://github.com/toatoes/hierarchical-clustering","last_synced_at":"2026-05-11T13:34:14.972Z","repository":{"id":258075394,"uuid":"867475859","full_name":"ToaToes/Hierarchical-Clustering","owner":"ToaToes","description":"This is a Hierarchical Clustering with a constant \"threshold\" that indicate the maximal distance between two clusters to group them. The algorithm stops when no cluster can be merged.","archived":false,"fork":false,"pushed_at":"2024-10-05T04:08:29.000Z","size":17,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-10T11:25:43.269Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Java","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ToaToes.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-10-04T06:23:15.000Z","updated_at":"2024-10-05T04:08:32.000Z","dependencies_parsed_at":"2024-10-17T15:10:30.962Z","dependency_job_id":null,"html_url":"https://github.com/ToaToes/Hierarchical-Clustering","commit_stats":null,"previous_names":["toatoes/hierarchical-clustering"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ToaToes%2FHierarchical-Clustering","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ToaToes%2FHierarchical-Clustering/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ToaToes%2FHierarchical-Clustering/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ToaToes%2FHierarchical-Clustering/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ToaToes","download_url":"https://codeload.github.com/ToaToes/Hierarchical-Clustering/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241018249,"owners_count":19895044,"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":[],"created_at":"2024-11-11T21:16:21.337Z","updated_at":"2026-05-11T13:34:14.939Z","avatar_url":"https://github.com/ToaToes.png","language":"Java","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Hierarchical-Clustering\n\u003cbr /\u003eThis is an implementation of generic Hierarchical Clustering Algorithm as described\n\u003cbr /\u003ein this webpage:\n```\nhttp://home.dei.polimi.it/matteucc/Clustering/tutorial_html/hierarchical.html\n```\n\u003cbr /\u003eThis is a Hierarchical Clustering with a constant \"threshold\" that indicate\n\u003cbr /\u003ethe maximal distance between two clusters to group them. The algorithm stops\n\u003cbr /\u003ewhen no cluster can be merged. \n\u003cbr /\u003eThe distance between two clusters is calculated as the distance between the\n\u003cbr /\u003emedians of the two clusters.\n\n\u003cbr /\u003erefer to:\n```\nhttp://mirlab.org/jang/books/dcpr/dcHierClustering.asp?title=3-2%20Hierarchical%20Clustering%20(%B6%A5%BCh%A6%A1%A4%C0%B8s%AAk)\n```\n\n\n\u003cbr /\u003eData Exploration: Understanding the dataset by clustering similar images can help in analyzing diversity and common features.\n\u003cbr /\u003eData Selection: If you have a large dataset, you can use clustering to select representative images from each cluster for training, thus reducing the size of the dataset while maintaining diversity.\n\u003cbr /\u003eFeature Extraction: You can cluster images based on feature representations from a pre-trained model (like a convolutional neural network), which can help identify similarities among images.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftoatoes%2Fhierarchical-clustering","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftoatoes%2Fhierarchical-clustering","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftoatoes%2Fhierarchical-clustering/lists"}