{"id":26219549,"url":"https://github.com/torodata/k-means-clustering-web-application","last_synced_at":"2026-03-16T12:03:05.083Z","repository":{"id":210578645,"uuid":"726914119","full_name":"ToroData/K-means-Clustering-Web-Application","owner":"ToroData","description":"Explore K-means clustering with my interactive web app. Visualize and cluster data points, learn its applications, and best practices.","archived":false,"fork":false,"pushed_at":"2023-12-03T19:14:47.000Z","size":7,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-12T14:18:31.665Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/ToroData.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,"governance":null}},"created_at":"2023-12-03T19:10:13.000Z","updated_at":"2023-12-03T19:14:51.000Z","dependencies_parsed_at":"2023-12-03T20:23:41.262Z","dependency_job_id":"4011cf8c-4a1e-48f7-9547-f09d9d92df38","html_url":"https://github.com/ToroData/K-means-Clustering-Web-Application","commit_stats":null,"previous_names":["torodata/k-means-clustering-web-application"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/ToroData/K-means-Clustering-Web-Application","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ToroData%2FK-means-Clustering-Web-Application","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ToroData%2FK-means-Clustering-Web-Application/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ToroData%2FK-means-Clustering-Web-Application/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ToroData%2FK-means-Clustering-Web-Application/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ToroData","download_url":"https://codeload.github.com/ToroData/K-means-Clustering-Web-Application/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ToroData%2FK-means-Clustering-Web-Application/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28029166,"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","status":"online","status_checked_at":"2025-12-25T02:00:05.988Z","response_time":58,"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":[],"created_at":"2025-03-12T14:18:33.143Z","updated_at":"2025-12-25T12:19:04.501Z","avatar_url":"https://github.com/ToroData.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# K-means-Clustering-Web-Application\n\n\u003cdiv align=\"center\"\u003e\n    \u003cimg src=\"https://thedatascientist.digital/img/logo.png\" alt=\"Logo\" width=\"25%\"\u003e\n\u003c/div\u003e\n\n\nThis web application demonstrates the K-means clustering algorithm, a fundamental technique in machine learning and data analysis. With an interactive and user-friendly interface, users can generate random data points, visualize them in 2D, and apply K-means clustering to group data points into clusters. The application provides insights into how K-means works, its real-world applications, and best practices for interpreting results.\n\n## Key Features:\n\n- Generate Random Data: Create random data points in 2D and 3D for clustering.\n- Interactive Visualization: Visualize data points, cluster centroids, and the clustering process.\n- Real-world Applications: Explore the use cases of K-means in customer segmentation, genetic data analysis, document classification, and image compression.\n- Easy-to-understand Tutorial: Learn about the algorithm's principles, its advantages, and how to interpret results.\n- Best Practices: Discover best practices for data normalization, choosing the number of clusters, and interpreting clusters effectively.\n\n## Technologies Used:\n\n- HTML, CSS, JavaScript for the web interface\n- D3.js for data visualization\n- Python (Flask) for server-side processing of K-means clustering\n\n\n## License\nThis project is licensed under the MIT License - see the [MIT LICENSE](https://choosealicense.com/licenses/mit/) file for details.\n\n\n## Author\n\n- [@RicardSantiagoRaigadaGarcía](https://www.thedatascientist.digital/)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftorodata%2Fk-means-clustering-web-application","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftorodata%2Fk-means-clustering-web-application","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftorodata%2Fk-means-clustering-web-application/lists"}