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: [...]

read more

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 [...]

read more

New recommender algorithms and evaluation

MOA has been extended in order to provide an interface to develop and visualize online recommender algorithms. This is a simple example in order to show the functionality of the EvaluateOnlineRecommender task in MOA. This task takes a rating predictor and a dataset (each training instance being a [user, item, rating] triplet) and evaluates how [...]

read more

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 [...]

read more

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: https://code.google.com/p/moa/ Cheers, The MOA Team

read more

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 http://www.bigdata-mining.org Key dates: Papers due: June 6th 23:59PM Acceptance notification: June 25, 2013 Workshop Final Paper Due: July 2, 2013 Paper submission [...]

read more

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 [...]

read more

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 (abifet@cs.waikato.ac.nz), we would love to hear what you think.     

read more

New release of MOA 12.08

We’ve made a new release of MOA 12.08. The new features of this release are: new rule classification methods : VFDR Rules from Learning Decision Rules from Data Streams, IJCAI 2011, J. Gama, P. Kosina migrated to proper maven project NaiveBayesMultinomial and SGD updated with adaptive DoubleVector for weights new multilabel classifiers: Scalable and efficient [...]

read more

CFP – Data Streams Track – ACM SAC 2013

============================================================ ACM SAC 2013 The 28th Annual ACM Symposium on Applied Computing in Coimbra, Portugal, March 18-22, 2013. http://www.acm.org/conferences/sac/sac2013/ DATA STREAMS TRACK http://www.cs.waikato.ac.nz/~abifet/SAC2013/ ============================================================ CALL FOR PAPERS The rapid development in information science and technology in general and in growth complexity and volume of data in particular has introduced new challenges for the research community. [...]

read more

Summer School on Massive Data Mining, August 8-10, 2012

August 8-10, 2012, IT University of Copenhagen, Denmark The summer school is aimed at PhD students and young researchers both from the algorithms community and the data mining community. A typical participant will be working in a group that aims at publishing in algorithms conferences such as ESA and SODA, and/or in data mining conferences [...]

read more

Big Data Mining (BigMine-12)

Call for Papers Big Data Mining (BigMine-12)1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BigMine-12) – a KDD2012 Workshop KDD2012 Conference Dates: August 12-16, 2012BigMine-12 Workshop Date: Aug 12, 2012Beijing, China http://www.bigdata-mining.org Key dates: Papers due: May 9, 2012 Acceptance notification: May 23, 2012Workshop Final Paper [...]

read more

New release of MOA 12.03

We’ve made a new release of MOA 12.03. The new features of this release are: new measure graphic visualization for classification Classifiers are now in subpackages: classifiers.tree, classifiers.bayes, classifiers.functions, classifiers.meta, classifiers.drift,… HoeffdingTree, HoeffdingTreeNB, and HoeffdingTreeNBAdaptive are now only one classifier: HoeffdingTree with an option to select how to do the classification at leaves. By default, [...]

read more

PRICAI 2012 Special Session on Scalable Big Data Mining

http://cs.waikato.ac.nz/~abifet/pricai2012/ September 3 – 7, 2012 Kuching, Sarawak, Malaysia ==============================================  CALL FOR PAPERS Data have become a torrent flowing in many important areas. Big data refers to datasets whose size is beyond the ability of current state-of-the art analytic tools. Streaming data is an specific approach to deal with big data that is evolving and [...]

read more

Upcoming Conference: “Machine-Learning with Real-time & Streaming Applications”

FIRST CONFERENCE ANNOUNCEMENT: From Data to Knowledge: Machine-Learning with Real-time & Streaming Applications May 7-11 2012 On the Campus of the University of California, Berkeley http://lyra.berkeley.edu/CDIConf/  * * CONFIRMED INVITED SPEAKERS * * Olfa Nasraoui (Louisville), Petros Drineas (RPI), Muthu Muthukrishnan (Rutgers), Alex Szalay (John Hopkins), David Bader (Georgia Tech), Eamonn Keogh (UC Riverside), Joao [...]

read more

IBLStreams (Instance Based Learner on Streams for Regression and Classification)

IBLStreams (Instance Based Learner on Streams) is an instance-based learning algorithm for classification and regression problems on data streams by Ammar Shaker, Eyke Hüllermeier and Jürgen Beringer. The method is able to handle large streams with low requirements in terms of memory and computational power. Moreover, it disposes of mechanisms for adapting to concept drift [...]

read more

New release of MOA 11.10

We’ve made a new release of MOA 11.10. The new features of this release are: new active classification methods : ActiveClassifier Cluster Mapping Measure CMM cleanup of Clustering Setup Panel export fix for FileStream based clusterings screenshot button: filename option wrapper for Weka Clustering algorithms You find the download link for this release on the [...]

read more

Pocket Data Mining Project using MOA

Pocket Data Mining PDM is a new term describing collaborative mining of streaming data in mobile and distributed computing environments by researchers Frederic Stahl, Mohamed Medhat Gaber, Max Bramer, and Philip S. Yu. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data [...]

read more

CFP – Data Streams Track – ACM SAC 2012

DATA STREAMS TRACKhttp://www.cs.waikato.ac.nz/~abifet/SAC2012/ ======================================================================== ACM SAC 2012 The 27th Annual ACM Symposium on Applied Computing in Trento University, Italy, March 20-23, 2012. http://www.acm.org/conferences/sac/sac2012/ ======================================================================== CALL FOR PAPERS The rapid development in information science and technology in general and in growth complexity and volume of data in particular has introduced new challenges for the research community. [...]

read more

HaCDAIS @ IEEE ICDM 2011

HaCDAIS 2011: The 2nd International Workshop on Handling Concept Drift in Adaptive Information Systems http://wwwis.win.tue.nl/hacdais2011/ CALL FOR PAPERS  In the real world data is often non stationary. In predictive analytics, machine learning and data mining the phenomenon of unexpected change in underlying data over time is known as concept drift. Changes in underlying data might [...]

read more