MOA is an open source framework for Big Data stream mining. It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.
I'm a bit stuck.... I have a range of conceptual, implementation and understanding questions.
[EASY] Where can I find the latest Javadoc?
[A bit history] Why do I see the mix between the yahoo/apache samoa, moa classes, and weka (e.g., InstanceHeader)??
3) [Impl] Little bit of context: I'm receiving data through a socket.io connection and distribute it directly through a Java rxJava Observable implementation == my stream. As long as I define the InstanceHeader for bootstrapping the algorithms e.g., myOutlierDetector.setModelContext(stream.getHeader()); and use (weka) Instances for the algorithm input I should be fine, right?
Hi,
I'm a bit stuck.... I have a range of conceptual, implementation and understanding questions.
myOutlierDetector.setModelContext(stream.getHeader());
and use (weka) Instances for the algorithm input I should be fine, right?Thanks