brCluster - Browse /OldFiles at SourceForge.net

The interactive file manager requires Javascript. Please enable it or use sftp or scp.
You may still browse the files here.

Name	Modified	Size	InfoDownloads / Week
Parent folder
brCluster_full_1.0.jar	2005-06-04	3.6 MB	0
brCluster_beta_full_1.0.jar	2005-06-03	3.6 MB	0
Totals: 2 Items		7.2 MB	0

Other Useful Business Software

Go from Code to Production URL in Seconds

Cloud Run deploys apps in any language instantly. Scales to zero. Pay only when code runs.

Skip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.

Start Free

Earn up to 16% annual interest with Nexo.

Access competitive interest rates on your digital assets.

Generate interest, borrow against your crypto, and trade a range of cryptocurrencies — all in one platform. Geographic restrictions, eligibility, and terms apply.

Get started with Nexo.

Build Agents and Models on One Platform

Everything you need to build production-ready agents and models. Access 200+ Google and third-party AI models and tools.

Gemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.

Start Free

Recommended Projects

JGAP
JGAP is a Genetic Algorithms and Genetic Programming package written in Java. It is designed to require minimum effort to use, but is also designed to be highly modular. JGAP features grid functionality and a lot of examples. Many unit tests included. Legal notice/Impressum: Klaus Meffert An der Struth 25 D-65510 Idstein sourceforge <at> klausmeffert.de
Scholar
Traditional machine learning on top of Nx
Modular toolkit for Data Processing MDP
The Modular toolkit for Data Processing (MDP) is a Python data processing framework. From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures. From the scientific developer's perspective, MDP is a modular framework, which can easily be expanded. The implementation of new algorithms is easy and intuitive. The new implemented units are then automatically integrated with the rest of the library. The base of available algorithms is steadily increasing and includes signal processing methods (Principal Component Analysis, Independent Component Analysis, Slow Feature Analysis), manifold learning methods ([Hessian] Locally Linear Embedding), several classifiers, probabilistic methods (Factor Analysis, RBM), data pre-processing methods, and many others.
MOA - Massive Online Analysis
Big Data Stream Analytics Framework.
Sampling-k-means
This project involves implementation and testing of sampling based algorithms the k-means clustering.

brCluster Files