Massive Online Analysis
Massive Online Analysis 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 experimentsof 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, 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
- ** 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
MOA supports bi-directional interaction with Weka. MOA is free software released under the GNU GPL.