Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Maintaining Stream Statistics over Sliding Windows
SIAM Journal on Computing
Data streams: algorithms and applications
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Clustering binary data streams with K-means
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
An Introduction to the Kalman Filter
An Introduction to the Kalman Filter
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Adaptive Learning from Evolving Data Streams
IDA '09 Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII
Adaptive learning and mining for data streams and frequent patterns
ACM SIGKDD Explorations Newsletter
Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Proceedings of the 2010 conference on Adaptive Stream Mining: Pattern Learning and Mining from Evolving Data Streams
Anomaly detection in streaming environmental sensor data: A data-driven modeling approach
Environmental Modelling & Software
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
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We study the combination of Kalman filter and a recently proposed algorithm for dynamically maintaining a sliding window, for learning from streams of examples. We integrate this idea into two well-known learning algorithms, the Naïve Bayes algorithm and the k-means clusterer. We show on synthetic data that the new algorithms do never worse, and in some cases much better, than the algorithms using only memoryless Kalman filters or sliding windows with no filtering.