New ensemble methods for evolving data streams
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Issues in evaluation of stream learning algorithms
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving Adaptive Bagging Methods for Evolving Data Streams
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Proceedings of the 2010 conference on Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Handling numeric attributes in hoeffding trees
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Leveraging bagging for evolving data streams
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Sentiment knowledge discovery in twitter streaming data
DS'10 Proceedings of the 13th international conference on Discovery science
Exploiting code redundancies in ECOC
DS'10 Proceedings of the 13th international conference on Discovery science
Mining frequent closed graphs on evolving data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
An effective evaluation measure for clustering on evolving data streams
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Accuracy updated ensemble for data streams with concept drift
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Batch weighted ensemble for mining data streams with concept drift
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Concurrent semi-supervised learning of data streams
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Active learning with evolving streaming data
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
MOA: a real-time analytics open source framework
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
MOA-TweetReader: real-time analysis in Twitter streaming data
DS'11 Proceedings of the 14th international conference on Discovery science
Controlled permutations for testing adaptive classifiers
DS'11 Proceedings of the 14th international conference on Discovery science
Online evaluation of email streaming classifiers using GNUsmail
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Stream management within the cloudminer
ICA3PP'11 Proceedings of the 11th international conference on Algorithms and architectures for parallel processing - Volume Part I
Mining Recurring Concept Drifts with Limited Labeled Streaming Data
ACM Transactions on Intelligent Systems and Technology (TIST)
Ensembles of Restricted Hoeffding Trees
ACM Transactions on Intelligent Systems and Technology (TIST)
Very Fast Decision Rules for multi-class problems
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Semi-supervised ensemble learning of data streams in the presence of concept drift
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Knowing: a generic data analysis application
Proceedings of the 15th International Conference on Extending Database Technology
AnyOut: anytime outlier detection on streaming data
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part I
Stream data mining using the MOA framework
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications - Volume Part II
Learning in non-stationary environments with class imbalance
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Heterogeneous ensemble for feature drifts in data streams
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Incrementally optimized decision tree for noisy big data
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Incrementally optimized decision tree for noisy big data
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Bayesian approach to the concept drift in the pattern recognition problems
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Multi-objective optimization for incremental decision tree learning
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
A single pass trellis-based algorithm for clustering evolving data streams
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
New management operations on classifiers pool to track recurring concepts
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
Handling time changing data with adaptive very fast decision rules
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Batch-incremental versus instance-incremental learning in dynamic and evolving data
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
On evaluating stream learning algorithms
Machine Learning
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
RCD: A recurring concept drift framework
Pattern Recognition Letters
Novelty detection algorithm for data streams multi-class problems
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Efficient data stream classification via probabilistic adaptive windows
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Mining big data: current status, and forecast to the future
ACM SIGKDD Explorations Newsletter
SAMOA: a platform for mining big data streams
Proceedings of the 22nd international conference on World Wide Web companion
Grand challenge: the TechniBall system
Proceedings of the 7th ACM international conference on Distributed event-based systems
Performance evaluation of incremental decision tree learning under noisy data streams
International Journal of Computer Applications in Technology
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Exploratory and interactive daily deals recommendation
Proceedings of the 7th ACM conference on Recommender systems
Data stream clustering: A survey
ACM Computing Surveys (CSUR)
Clustering spatial data streams for targeted alerting in disaster response
Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoStreaming
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
Energy-based function to evaluate data stream clustering
Advances in Data Analysis and Classification
Twitter spammer detection using data stream clustering
Information Sciences: an International Journal
Combining block-based and online methods in learning ensembles from concept drifting data streams
Information Sciences: an International Journal
A similarity-based approach for data stream classification
Expert Systems with Applications: An International Journal
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Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA includes a collection of offline and online methods as well as tools for evaluation. In particular, it implements boosting, bagging, and Hoeffding Trees, all with and without Naïve Bayes classifiers at the leaves. MOA supports bi-directional interaction with WEKA, the Waikato Environment for Knowledge Analysis, and is released under the GNU GPL license.