Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Maintaining Stream Statistics over Sliding Windows
SIAM Journal on Computing
A framework for diagnosing changes in evolving data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Sketch-based change detection: methods, evaluation, and applications
Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement
A martingale framework for concept change detection in time-varying data streams
ICML '05 Proceedings of the 22nd international conference on Machine learning
Stable distributions, pseudorandom generators, embeddings, and data stream computation
Journal of the ACM (JACM)
Data Streams: Models and Algorithms (Advances in Database Systems)
Data Streams: Models and Algorithms (Advances in Database Systems)
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Detecting change in data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Sequential Change Detection on Data Streams
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Optimal Window Change Detection
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Mining Frequent Itemsets in a Stream
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
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In data stream mining many algorithms are based on fixed size sliding windows to cope with the aging of data. This despite of some flaws of fixed size windows. Namely, it is difficult to set the size of the window and there does not exist an optimal window size due to different types of changes in the underlying distribution of the data stream. Because of these reasons the algorithm performance degrades. We propose some initial steps toward efficiently equipping sliding window algorithms with flexible windowing. This is done by the efficient maintenance of a statistic called, the maximum normalized mean. This statistic is maximized over all time windows and thus uses flexible windowing. We show that several algorithms can be restated such that it uses the maximum normalized mean as a building block. The usefulness of the normalized mean in the context of these algorithms is shown by means of experiments.