New Release of MOA 16.04

We’ve made a new release of MOA 16.04. The new features of this release are: BICO: BIRCH Meets Coresets for k-Means Clustering. Hendrik Fichtenberger, Marc Gillé, Melanie Schmidt, Chris Schwiegelshohn, Christian Sohler: ESA 2013: 481-492 (2013) Updates: MultiLabel and MultiTarget methods There are these important changes after MOA 2015.11 release: Use Examples instead of […]

Prequential Cross-Validation Evaluation

In data stream classification, the most used evaluation is the prequential one, where instances are first used to test, and then to train. However, the weakness of prequential evaluation compared to cross-validation was that it was running only one experiment. We are proud to announce that MOA now contains a new prequential cross-validation evaluation with the […]

New Release of MOA 15.11

We’ve made a new release of MOA 15.11. The new features of this release are: iSOUPTree. Aljaz Osojnik, Pance Panov, Saso Dzeroski: Multi-label Classification via Multi-target Regression on Data Streams. Discovery Science 2015: 170-185 SEEDChangeDetector. David Tse Jung Huang, Yun Sing Koh, Gillian Dobbie, Russel Pears: Detecting Volatility Shift in Data Streams. ICDM 2014: 863-868 […]

New Release of MOA 14.11

We’ve made a new release of MOA 14.11. The new features of this release are: Lazy kNN methods. Albert Bifet, Bernhard Pfahringer, Jesse Read, Geoff Holmes: Efficient data stream classification via probabilistic adaptive windows. SAC 2013: 801-806 SGDMultiClass for multi-class SGD learning. OnlineSmoothBoost Shang-Tse Chen, Hsuan-Tien Lin, Chi-Jen Lu:An Online Boosting Algorithm with Theoretical Justifications. […]

Using MOA from ADAMS workflow engine

MOA and WEKA are powerful tools to perform data mining analysis tasks. Usually, in real applications and professional settings, the data mining processes are complex and consist of several steps. These steps can be seen as a workflow. Instead of implementing a program in JAVA, a professional data miner will build a solution using a […]

OpenML: exploring machine learning better, together. Now you can use MOA classifiers inside OpenML. OpenML is a website where researchers can share their datasets, implementations and experiments in such a way that they can easily be found and reused by others. OpenML engenders a novel, collaborative approach to experimentation with important benefits. First, many questions about machine learning algorithms won’t […]

SAMOA: Scalable Advanced Massive Online Analysis SAMOA is distributed streaming machine learning (ML) framework that contains a programing abstraction for distributed streaming ML algorithms. It is a project started at Yahoo Labs Barcelona. SAMOA enables development of new ML algorithms without dealing with the complexity of underlying streaming processing engines (SPE, such as Apache Storm and Apache S4). SAMOA users […]

RMOA: Massive online data stream classifications with R & MOA For R users who work with a lot of data or encounter RAM issues when building models on large datasets, MOA and in general data streams have some nice features. Namely: It uses a limited amount of memory. So this means no RAM issues when building models. Processes one example at a time, and […]

The streams Framework The streams framework is a Java implementation of a simple stream processing environment by Christian Bockermann and Hendrik Blom at TU Dortmund University. It aims at providing a clean and easy-to-use Java-based platform to process streaming data. The core module of the streams library is a thin API layer of interfaces and classes that […]

New Release of MOA 14.04

We’ve made a new release of MOA 14.04. The new features of this release are: Change detection Tab Albert Bifet, Jesse Read, Bernhard Pfahringer, Geoff Holmes, Indre Zliobaite: CD-MOA: Change Detection Framework for Massive Online Analysis. IDA 2013: 92-103 New Tutorial on Clustering by Frederic Stahl. New version of Adaptive Model Rules for regression Ezilda […]

New release of MOA 13.11

We’ve made a new release of MOA 13.11. The new feature of this release is: Temporal dependency evaluation Albert Bifet, Jesse Read, Indre Zliobaite, Bernhard Pfahringer, Geoff Holmes: Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them. ECML/PKDD (1) 2013: 465-479 You find the download link for this release on the MOA homepage: […]

Temporal Dependency in Classification

The paper presented at ECML-PKDD 2013 titled “Pitfalls in benchmarking data stream classification and how to avoid them“, showed that classifying data streams has an important temporal component, which we are currently not considering in the evaluation of data-stream classifiers. A very simple classifier that considers this temporal component, the non-change classifier that predicts only […]

New release of MOA 13.08

We’ve made a new release of MOA 13.08. The new features of this release are: new outlier detection tab Dimitrios Georgiadis, Maria Kontaki, Anastasios Gounaris, Apostolos N. Papadopoulos, Kostas Tsichlas, Yannis Manolopoulos: Continuous outlier detection in data streams: an extensible framework and state-of-the-art algorithms. SIGMOD Conference 2013: 1061-1064 new regression tab FIMT-DD regression tree Elena […]

Pre-release of MOA 13.08

We are preparing a new release of MOA 13.08. The new release of MOA will contain the FIMT-DD regression tree, the Adaptive Model Rules, a recommender system based in BRISMFPredictor, and some more features in clustering and a new outlier detection tab. You will find the source code at the repository: Cheers, The MOA Team

CFP – KDD BIGMINE Workshop on Big Data Mining

Big Data Mining 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BigMine-13) Conference Dates: August 11-14, 2013 Workshop Date: Aug 11, 2013 Chicago, USA Key dates: Papers due: June 6th 23:59PM Acceptance notification: June 25, 2013 Workshop Final Paper Due: July 2, 2013 Paper submission […]

ADAMS – a different take on workflows

A fascinating new workflow for MOA and Weka is available. The Advanced Data mining And Machine learning System (ADAMS) is a novel, flexible workflow engine aimed at quickly building and maintaining real-world, complex knowledge workflows. It is written in Java and uses Maven as its build system. The framework was open-sourced in September 2012, released […]

Are you using MOA?

Are you using MOA?  Are you doing some interesting stuff using MOA? What are your thoughts about MOA and its future?  We would love to hear your experiences with MOA. Please send us a message (, we would love to hear what you think.