MOA (Massive Online Analysis) is a framework for data stream mining and online machine learning. It provides algorithms and experimental facilities for analysing data that arrives continuously, rather than requiring the entire dataset to be available before processing begins.
This approach is useful for applications such as network monitoring, sensor analysis and other situations where information changes over time. Unlike conventional batch learning, online algorithms update their models as new observations arrive, often while operating with limited memory.
MOA is particularly valuable for research and teaching. Its experimental environment allows different learning methods to be compared under controlled conditions, including situations where the statistical properties of a stream change over time. The framework also interoperates with Weka and supports the development of additional algorithms and evaluation procedures.
This is free and open source software.
Key Features
- Online classification algorithms including incremental decision trees, Bayesian methods and ensemble classifiers.
- Regression algorithms for predicting numerical values from evolving data streams.
- Stream clustering methods including CluStream, ClusTree and StreamKM++.
- Outlier detection for identifying unusual observations in streaming data.
- Concept drift detection for identifying changes in the statistical properties of incoming observations.
- Adaptive learning methods that respond to changing data distributions.
- Prequential evaluation, allowing models to be tested and updated as observations arrive.
- Configurable synthetic stream generators for benchmarking learning algorithms.
- Support for experiments using real-world streaming datasets.
- Tools for evaluating classification, regression and clustering performance.
- Graphical interface for configuring algorithms and running experiments.
- Command-line execution for repeatable experiments and automated evaluation.
- Integration with Weka for combining online and batch learning approaches.
- Extensible architecture supporting additional learners, stream generators and evaluation measures.
Website: moa.cms.waikato.ac.nz
Support:
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 |
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