• Source: Massive Online Analysis

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 (machine learning). MOA is free software released under the GNU GPL.




See also



ADAMS Workflow: Workflow engine for MOA and Weka (machine learning)
Streams: Flexible module environment for the design and execution of data stream experiments
Weka (machine learning)
Vowpal Wabbit
List of numerical analysis software




References






External links


MOA Project home page at University of Waikato in New Zealand
SAMOA Project home page at Yahoo Labs

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