Massive Online Analysis

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 (machine learning). MOA is free software released under the GNU GPL.