MOA: Massive Online Analysis, a Framework for Stream Classification and Clustering
Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen, Thomas Seidl.
Journal of Machine Learning Research (JMLR) Workshop and Conference Proceedings.
Volume 11: Workshop on Applications of Pattern Analysis (2010).
(download the extended version here.)
Mining Frequent Closed Graphs on Evolving Data Streams
Albert Bifet, Geoff Holmes, Bernhard Pfahringer and Ricard Gavaldà.
In 17th ACM SIGKDD Intl. Conference on Knowledge Discovery and Data Mining KDD’11.
An Effective Evaluation Measure for Clustering on Evolving Data Stream.
Hardy Kremer, Philipp Kranen, Timm Jansen, Thomas Seidl, Albert Bifet, Geoff Holmes and Bernhard Pfahringer.
In 17th ACM SIGKDD Intl. Conference on Knowledge Discovery and Data Mining KDD’11.
Active learning with evolving streaming data
Indrė Žliobaitė, Albert Bifet, Bernhard Pfahringer and Geoff Holmes.
In Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2011.
MOA: a Real-time Analytics Open Source Framework
Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Philipp Kranen, Hardy Kremer, Timm Jansen, and Thomas Seidl.
Demo at Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2011
Leveraging Bagging for Evolving Data Streams
Albert Bifet, Geoff Holmes, and Bernhard Pfahringer.
In Machine Learning and Knowledge Discovery in Databases, European Conference ECML PKDD 2010 .
Sentiment knowledge discovery in Twitter streaming data
Albert Bifet and Eibe Frank.
In Proc 13th International Conference on Discovery Science, Canberra, Australia, 2010
Fast Perceptron Decision Tree Learning from Evolving Data Streams
Albert Bifet, Geoff Holmes, Bernhard Pfahringer, and Eibe Frank.
In Advances in Knowledge Discovery and Data Mining, 14th Pacific-Asia Conference, PAKDD 2010.
MOA: Massive Online Analysis
Albert Bifet, Geoff Holmes, Richard Kirkby and Bernhard Pfahringer,
In Journal of Machine Learning Research 11, 1601-1604. , 2010.
Accurate ensembles for data streams: Combining restricted Hoeffding trees using stacking
Albert Bifet, Eibe Frank, Geoff Holmes, and Bernhard Pfahringer.
In Proc 2nd Asian Conference on Machine Learning, Tokyo. JMLR, 2010 .
Improving Adaptive Bagging Methods for Evolving Data Streams
Albert Bifet, Geoff Holmes, Bernhard Pfahringer, and Ricard Gavaldà
In First Asian Conference on Machine Learning, ACML 2009, Nanjing, China, November 2009. .
New ensemble methods for evolving data streams
Albert Bifet, Geoff Holmes, Bernhard Pfahringer, Richard Kirkby, and Ricard Gavaldà.
In 15th ACM SIGKDD Intl. Conference on Knowledge Discovery and Data Mining (KDD’09), 2009.
Handling Numeric Attributes in Hoeffding Trees
Bernhard Pfahringer, Geoffrey Holmes, and Richard Kirkby.
In Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2008.
New Options for Hoeffding Trees
Bernhard Pfahringer, Geoffrey Holmes, and Richard Kirkby.
In Australian Conference on Artificial Intelligence, pages
90-99, 2007.
Tie Breaking in Hoeffding trees
Geoffrey Holmes, Richard Kirkby, and Bernhard Pfahringer.
In J. Gama and J. S. Aguilar-Ruiz, editors, Proc Workshop W6:
Second International Workshop on Knowledge Discovery in Data Streams, pages
107-116, 2005.
Cache hierarchy inspired compression: a novel architecture for data streams
Geoffrey Holmes, Bernhard Pfahringer, and Richard Kirkby.
In Narayanan Kulathuramaiyer, Alvin W. Yeo, Wang Yin Chai, and
Tan Chong Eng, editors, Proc Fourth International Conference on
Information Technology in Asia (CITA’05), pages 130-136, 2005.
12-15 December 2005.
Stress-Testing Hoeffding Trees
Geoffrey Holmes, Richard Kirkby, and Bernhard Pfahringer.
In Proc 9th European Conference on Principles and Practice of
Knowledge Discovery in Databases, Porto, Portugal, pages 495-502. Springer,
2005.
