MOA (Massive Online Analysis) is a popular open source for software environment for implementing algorithms and running experiments for online learning from evolving data streams.
It includes a collection of machine learning algorithms (classification, regression, clustering, outlier detection, concept drift detection and recommender systems) and tools for evaluation.
MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, which is an award-winning open-source workbench containing implementations of a wide range of batch machine learning methods.
The software is developed in the Java programming language.
Key Features
- Classification:
- Bayesian classifiers.
- Naive Bayes.
Naive Bayes Multinomial.
- Naive Bayes.
- 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 .
- Bayesian 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.
Website: moa.cms.waikato.ac.nz
Support: Documentation, Mailing List (Users), Mailing List (Developers), GitHub Code Repository
Developer: University of Waikato
License: GNU General Public License v3.0
MOA is written in Java. Learn Java with our recommended free books and free tutorials.
Related Software
| Data Mining Software | |
|---|---|
| R | Software environment for statistical computing and graphics |
| MOA | Software environment for data stream mining |
| Orange | Component-based framework for machine learning and data mining |
| astroML | Python module for machine learning and data mining |
| ROOT | Aimed at solving the data analysis challenges of high-energy physics |
| ELKI | Data mining software framework developed for use in research and teaching |
| DataMelt | Full-featured data-analysis framework for scientists, engineers and students |
| KNIME | Konstanz Information Miner |
| Weka | Waikato Environment for Knowledge Analysis |
| RapidMiner | Knowledge discovery in databases, machine learning, and data mining |
| Rattle | Gnome cross platform GUI for Data Mining using R |
Read our verdict in the software roundup.
 Explore our comprehensive directory of recommended free and open source software. Our carefully curated collection spans every major software category.This directory is part of our ongoing series of informative articles for Linux enthusiasts. It features hundreds of detailed reviews, along with open source alternatives to proprietary solutions from major corporations such as Google, Microsoft, Apple, Adobe, IBM, Cisco, Oracle, and Autodesk. You’ll also find interesting projects to try, hardware coverage, free programming books and tutorials, and much more. Discovered a useful open source Linux program that we haven’t covered yet? Let us know by completing this form. |