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MOA (Massive Online Analysis) is a free open-source software specific for Data stream mining with Concept drift. It's 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 * * 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. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Massive Online Analysis」の詳細全文を読む スポンサード リンク
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