{"id":13471567,"url":"https://github.com/aws/random-cut-forest-by-aws","last_synced_at":"2025-05-15T10:07:43.186Z","repository":{"id":36769575,"uuid":"221992547","full_name":"aws/random-cut-forest-by-aws","owner":"aws","description":"An implementation of the Random Cut Forest data structure for sketching streaming data, with support for anomaly detection, density estimation, imputation, and more.","archived":false,"fork":false,"pushed_at":"2024-10-10T18:40:53.000Z","size":3305,"stargazers_count":224,"open_issues_count":15,"forks_count":34,"subscribers_count":11,"default_branch":"main","last_synced_at":"2025-05-08T00:08:04.314Z","etag":null,"topics":["algorithms","anomalydetection","streaming"],"latest_commit_sha":null,"homepage":"https://github.com/aws/random-cut-forest-by-aws","language":"Java","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/aws.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","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":"2019-11-15T19:48:13.000Z","updated_at":"2025-05-06T17:22:56.000Z","dependencies_parsed_at":"2023-02-18T01:15:47.890Z","dependency_job_id":"5d9ac590-e103-4172-894a-f1818cd13d34","html_url":"https://github.com/aws/random-cut-forest-by-aws","commit_stats":{"total_commits":175,"total_committers":20,"mean_commits":8.75,"dds":0.6171428571428572,"last_synced_commit":"35f4cf6cfbe9e2ac0a9b35b88034800f36bec181"},"previous_names":[],"tags_count":16,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aws%2Frandom-cut-forest-by-aws","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aws%2Frandom-cut-forest-by-aws/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aws%2Frandom-cut-forest-by-aws/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/aws%2Frandom-cut-forest-by-aws/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/aws","download_url":"https://codeload.github.com/aws/random-cut-forest-by-aws/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254319720,"owners_count":22051073,"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":["algorithms","anomalydetection","streaming"],"created_at":"2024-07-31T16:00:46.732Z","updated_at":"2025-05-15T10:07:38.168Z","avatar_url":"https://github.com/aws.png","language":"Java","funding_links":[],"categories":["Java","人工智能"],"sub_categories":[],"readme":"# Random Cut Forest by AWS\n\nThis repository contains implementations of the Random Cut Forest (RCF) probabilistic data structure.\nRCFs were originally developed at Amazon to use in a nonparametric anomaly detection algorithm for\nstreaming data. Later new algorithms based on RCFs were developed for density estimation, imputation,\nand forecasting.\n\nThe different directories correspond to equivalent implementations in different languages, and bindings to\nto those base implementations, using language specific features for greater flexibility of use. \n\nRandomCutForest in the randomcutforest-core package provides an estimation (say anomaly score, or extrapolation over a forecast horizon)\nand using that raw estimation can be challenging. The randomcutforest-parkservices package provides\nseveral capabilities (ThresholdedRandomCutForest, RCFCaster, respectively) for distilling the scores to a determination of\na potential anomaly or calibrated forecast respectively.\nThe package randomcutforest-examples showcases several example scenarios for using the repository. \nThey also provide examples for some of the parameter settings. Many of these examples are built in tests.\n\n## Documentation\n\n* Guha, S., Mishra, N., Roy, G., \u0026 Schrijvers, O. (2016, June). Robust random cut forest based anomaly detection on streams. In *International conference on machine learning* (pp. 2712-2721).\n\n## Code of Conduct\n\nThis project has adopted an [Open Source Code of Conduct](https://aws.github.io/code-of-conduct).\n\n\n## Security issue notifications\n\nIf you discover a potential security issue in this project we ask that you notify AWS/Amazon Security via our [vulnerability reporting page](http://aws.amazon.com/security/vulnerability-reporting/). Please do **not** create a public GitHub issue.\n\n\n## Licensing\n\nSee the [LICENSE](./LICENSE) file for our project's licensing. We will ask you to confirm the licensing of your contribution.\n\n\n## Copyright\n\nCopyright 2019-2020 Amazon.com, Inc. or its affiliates. All Rights Reserved.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faws%2Frandom-cut-forest-by-aws","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faws%2Frandom-cut-forest-by-aws","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faws%2Frandom-cut-forest-by-aws/lists"}