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
References
External links