Active Mining of Data Streams

13 years 6 months ago
Active Mining of Data Streams
Most previously proposed mining methods on data streams make an unrealistic assumption that "labelled" data stream is readily available and can be mined at anytime. However, in most real-world problems, labelled data streams are rarely immediately available. Due to this reason, models are refreshed periodically, that is usually synchronized with data availability schedule. There are several undesirable consequences of this "passive periodic refresh". In this paper, we propose a new concept of demand-driven active data mining. It estimates the error of the model on the new data stream without knowing the true class labels. When significantly higher error is suspected, it investigates the true class labels of a selected number of examples in the most recent data stream to verify the suspected higher error. 1 State-of-the-art Stream Mining State-of-the-art work on mining data streams concentrates on capturing time-evolving trends and patterns with "labeled" ...
Wei Fan, Yi-an Huang, Haixun Wang, Philip S. Yu
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2004
Where SDM
Authors Wei Fan, Yi-an Huang, Haixun Wang, Philip S. Yu
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