Massive Online Analysis
A framework for data stream mining including machine learning algorithms such as classification, regression, clustering, outlier detection, concept drift detection and recommender systems and tools for evaluation
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| + | Online Learning from Evolving Data Streams | Allows implementing algorithms and conducting experiments for online learning from data streams that evolve over time. |
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| + | Collection of Offline and Online Methods | Includes a variety of machine learning methods, both offline and online, suitable for data stream analysis. |
| + | Boosting and Bagging Algorithms | Supports boosting and bagging techniques for ensemble learning. |
| + | Hoeffding Trees | Provides Hoeffding Trees, a type of decision tree designed for streaming data. |
| + | Naïve Bayes Classifiers | Integrates Naïve Bayes classifiers into its boosting and bagging algorithms. |
| + | Bi-Directional Interaction with WEKA | Interacts with WEKA, another open-source workbench for machine learning, enhancing its capabilities. |
| + | Memory-Efficient Processing | Processes examples one at a time, using limited memory resources. |
| + | Real-Time Prediction | Ready to predict class labels for unseen examples at any time. |
| + | Data Stream Mining | Specializes in mining data streams, handling high-speed data arrival. |
...10 more features/limitations. Contact us to get a complete list of features and system requirements.
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Written in
Java
Initial Release
2009-06-28
Repository
License
Categories
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Notes
- Release after thesis-release is counted as initial release.