Debellor - Large Scale Data Mining and Machine Learning
Debellor simplifies implementation of complex yet efficient algorithms.
- 120+ algorithms are already available and can be used as building blocks. These include all classifiers from Weka and Rseslib libraries, all filters from Weka and a reader of arff files.
- Simplicity. All algorithms are accessible through the same simple interface of a Cell.
- Scalability. Thanks to stream architecture algorithms may process data on the fly instead of keeping all of them in memory. This enables efficient handling of large volumes of data and gives you freedom of designing arbitrarily complex processing networks.
- Extendibility. You may define new data types, specific to your application domain. See DataObject and DataType.
- Multithreading. Take full advantage of a multi-core CPU with parallel execution of experiment. Debellor takes care of thread management and synchronization, you only decides where to make a splitting point between threads.
- Extensibility of algorithm pool through well-defined API (see Cell class).
- Extensibility of types of data that algorithms operate on (see Data class).
- Stream-based data processing, for efficient handling of large volumes of data and for freedom of designing complex experiments, which may generate large volumes of data at intermediate stages.