RapidMiner Community Edition
|Op. System:||Windoows All|
|File size:||49.08 MB|
Publisher description for RapidMiner Community Edition
RapidMiner is the world-wide leading data mining solution due to the combination of its functional range and its leading-edge technologies. Applications of RapidMiner have a wide spread in data mining all over the world. Use RapidMiner and explore your data! Simplify the construction of experiments and the evaluation of different approaches. Try to find the best combination of preprocessing and learning steps or let RapidMiner do that automatically for you. FEATURES: 100% pure Java (runs on every major platform and operating system) KD processes are modeled as simple operator trees which is both intuitive and powerful operator trees or subtrees can be saved as building blocks for later re-use internal XML representation ensures standardized interchange format of data mining experiments simple scripting language allowing for automatic large-scale experiments multi-layered data view concept ensures efficient and transparent data handling Flexibility in using RapidMiner: graphical user interface (GUI) for interactive prototyping command line mode (batch mode) for automated large-scale applications Java API (application programming interface) to ease usage of RapidMiner from your own programs simple plugin and extension mechanisms, a broad variety of plugins already exists and you can easily add your own powerful plotting facility offering a large set of sophisticated high-dimensional visualization techniques for data and models more than 400 machine learning, evaluation, in- and output, pre- and post-processing, and visualization operators plus numerous meta optimization schemes machine learning library WEKA fully integrated (WEKA web page) RapidMiner was successfully applied on a wide range of applications where its rapid prototyping abilities demonstrated their usefulness, including text mining, multimedia mining, feature engineering, data stream mining and tracking drifting concepts, development of ensemble methods, and distributed data mining.