Massive Online Analysis MOA
Massive Online Analysis is 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
Features
MOA (Massive Online Analysis) is an open source 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. MOA is written in Java and relates to WEKA project.
MOA allows to build and run experiments of machine learning or data mining on evolving data streams. It is also possible to use WEKA classifiers from MOA, and MOA classifiers and streams from WEKA.
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Developer
Albert Bifet, Geoff Holmes, Bernhard Pfahringer, University of Waikato, Other contributors
Written in
Java
Initial Release
28 June 2009
Repository
License
GPL v3
Categories
Alternatives
Machine Learning
Apache Mahout
Apache MXNet (Incubating)
Apache Spark
Apache SystemML
Eclipse Deeplearning4j
MALLET
mlpack
OpenCV
Orange
PyTorch
scikit-learn
TensorFlow
The Microsoft Cognitive Toolkit
Torch
Weka
Yooreeka
Data Mining
ELKI
OpenNN
Orange
scikit-learn
Weka
Yooreeka
Notes
- Release after thesis-release is counted as initial release.
This page was last updated with commit: Following: - Fixed: missing sources for features now added - Removed: Google Analytics Async (deprecated) - Added: missing aria-labels to input elements - Updated: partials/seo.html code for new data structure - Fixed: changed aria-label to title for span and divs - Fixed: color of status icon on softpages not appearing correctly (5221a6e)