MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering.

1st Workshop on Applications of Pattern Analysis

计算机科学;数学科学

Albert Bifet [e-mail] ; Bernhard Pfahringer [e-mail] ; Hardy Kremer [e-mail] ; Thomas Seidl [e-mail]

PID  :  116665

来源: CEUR

【 摘 要 】

Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed to deal with the challenging problem of scaling up the implementation of state of the art algorithms to real world dataset sizes. It contains collection of oine and online for both classification and clustering as well as tools for evaluation. In particular, for classification it implements boosting, bagging, and Hoeffding Trees, all with and without Nave Bayes classifiers at the leaves. For clustering, it implements StreamKM++, CluStream, ClusTree, DenStream, DStream and CobWeb. Researchers benefit from MOA by getting insights into workings and problems of different approaches, practitioners can easily apply and compare several algorithms to real world data set and settings. MOA supports bidirectional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and is released