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scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
DBSCAN* [6] [7] is a variation that treats border points as noise, and this way achieves a fully deterministic result as well as a more consistent statistical interpretation of density-connected components. The quality of DBSCAN depends on the distance measure used in the function regionQuery(P,ε).
The R package "dbscan" includes a C++ implementation of OPTICS (with both traditional dbscan-like and ξ cluster extraction) using a k-d tree for index acceleration for Euclidean distance only. Python implementations of OPTICS are available in the PyClustering library and in scikit-learn. HDBSCAN* is available in the hdbscan library.
The following implementations are available under Free/Open Source Software licenses, with publicly available source code. Accord.NET contains C# implementations for k-means, k-means++ and k-modes. ALGLIB contains parallelized C++ and C# implementations for k-means and k-means++. AOSP contains a Java implementation for k-means.
The Java just-in-time compiler optimizes all combinations to a similar extent, making benchmarking results more comparable if they share large parts of the code. When developing new algorithms or index structures, the existing components can be easily reused, and the type safety of Java detects many programming errors at compile time.
List of GitHub repositories of the project: Cloud Native Computing Foundation (CNCF) This data is not pre-processed List of GitHub repositories of the project: Operator Framework This data is not pre-processed List of GitHub repositories of the project [408] GitHub repositories referenced in artifacthub.io This data is not pre-processed
This is a list of proprietary source-available software, which has available source code, but is not classified as free software or open-source software. In some cases, this type of software is originally sold and released without the source code , and the source code becomes available later.
Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. [1] Other frameworks in the spectrum of supervisions include weak- or semi-supervision, where a small portion of the data is tagged, and self-supervision.