{"id":20187431,"url":"https://github.com/michaelb/point-clustering","last_synced_at":"2026-06-09T15:01:45.424Z","repository":{"id":104285639,"uuid":"246383633","full_name":"michaelb/point-clustering","owner":"michaelb","description":"Regroup points in a nth-dimension space if they are closer than a certain distance","archived":false,"fork":false,"pushed_at":"2020-05-10T10:31:35.000Z","size":21403,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2026-03-01T13:46:05.248Z","etag":null,"topics":["clustering-algorithm","dimensions"],"latest_commit_sha":null,"homepage":"","language":"Python","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/michaelb.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":"2020-03-10T18:58:20.000Z","updated_at":"2020-07-29T16:03:17.000Z","dependencies_parsed_at":"2023-03-09T10:00:13.485Z","dependency_job_id":null,"html_url":"https://github.com/michaelb/point-clustering","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/michaelb/point-clustering","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/michaelb%2Fpoint-clustering","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/michaelb%2Fpoint-clustering/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/michaelb%2Fpoint-clustering/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/michaelb%2Fpoint-clustering/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/michaelb","download_url":"https://codeload.github.com/michaelb/point-clustering/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/michaelb%2Fpoint-clustering/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":34112225,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-06-09T02:00:06.510Z","response_time":63,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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-algorithm","dimensions"],"created_at":"2024-11-14T03:23:45.283Z","updated_at":"2026-06-09T15:01:45.405Z","avatar_url":"https://github.com/michaelb.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"Project Algo\n===========\n\nAuthors: Michael Bleuez and a friend who may want to remain anonymous\n--------------------------------------------\n\n**Goal**:\nThe project aims to find the size of \"clusters\" within a set of points.\n(A cluster is a connex composant, 2 points being 'in contact' iff they are within a given distance of each other)\n\n**Performance**:\n* A perfomance table (versions of the program, input format and execution time) is available in math/perfs.ods\n* Complexity is roughly of __O(n.log(n).a^k)__ with n the number of points, where a~2.2 and k is the dimension of input space,\n\thowever actual execution time vary a lot depending on properties of input;\n\t1. how much there are points interlinked (big clusters are detrimental in general) (or how big is distance relative to number of points)\n\t2. randomness of the distribution: uniformly distributed allow faster resolution, **to a big extent**\n* Real-world speed: at this point of the project, our algorithm can process any reasonable (random-like, 2D) input of size 20k in ~0.5s (i5 4210U 1.7Ghz, SATA SSD)\n\tIt is really hard to create a non-random distribution that is really the *worst* possible, but we have been able to slow the algorithm up to 60 sec (still 20k points.)\n\tFor reference a 100% naïve algorithm take up to 8 minutes to solve (any) 20k-sized input.\n\n\n**Etymology**:\n* cluster: are \"connex composant\", is a class of objects. Cluster object include reference to the points they contain, which themselves know which cluster they are a part of\n* quadrillage: divide the space in \"cases\" \n* points: are given a reference to an unique to a cluster object (containing only said point at first) at their creation. merge is done via merge method of cluster object\n* density: relative to the given *distance*, how much the space is 'crowded'. A good exemple is that same-density sets have clusters of same ratio (size of cluster)/(total number of points) \n* a-types: are input where the points are quite sparse (relative to the given distance); an a-type input will contain only few tuples and a pletoria of singletons\n* b-types: are inputs contains too much points relative to the given distance, thus is usually one extra large cluster and a few others\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmichaelb%2Fpoint-clustering","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fmichaelb%2Fpoint-clustering","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fmichaelb%2Fpoint-clustering/lists"}