Massive Online Analysis (MOA) is a free open-source software project specific for data stream mining with concept drift. It is written in Java and developed at the University of Waikato, New Zealand.
Description MOA is an open-source framework software that allows to build and run experiments of machine learning or data mining on evolving data streams. It includes a set of learners and stream generators that can be used from the graphical user interface (GUI), the command-line, and the Java API. MOA contains several collections of machine learning algorithms:
Classification Bayesian classifiers Naive Bayes Naive Bayes Multinomial Decision trees classifiers Decision Stump Hoeffding Tree Hoeffding Option Tree Hoeffding Adaptive Tree Meta classifiers Bagging Boosting Bagging using ADWIN Bagging using Adaptive-Size Hoeffding Trees. Perceptron Stacking of Restricted Hoeffding Trees Leveraging Bagging Online Accuracy Updated Ensemble Function classifiers Perceptron Stochastic gradient descent (SGD) Pegasos Drift classifiers Self-Adjusting Memory Probabilistic Adaptive Windowing Multi-label classifiers Active learning classifiers Regression FIMTDD AMRules Clustering StreamKM++ CluStream ClusTree D-Stream CobWeb. Outlier detection STORM Abstract-C COD MCOD AnyOut Recommender systems BRISMFPredictor Frequent pattern mining Itemsets Graphs Change detection algorithms These algorithms are designed for large scale machine learning, dealing with concept drift, and big data streams in real time. MOA supports bi-directional interaction with Weka. MOA is free software released under the GNU GPL.
See also
ADAMS Workflow: Workflow engine for MOA and Weka Streams: Flexible module environment for the design and execution of data stream experiments Vowpal Wabbit List of numerical analysis software
References
External links MOA Project home page at University of Waikato in New Zealand SAMOA Project home page at Yahoo Labs