Abstract: Many interesting real-world applications for temporal data mining are hindered by concept drift. One particular form of concept drift is ...
May 22, 2006 · Wenerstrom, Brent K., "Temporal Data Mining in a Dynamic Feature Space" (2006). Theses and. Dissertations. 761. https://scholarsarchive.byu ...
Temporal Data Mining in Dynamic Feature Spaces | Request PDF
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Advanced analysis of data streams is quickly becoming a key area of data mining research as the number of applications demanding such processing increases.
Many interesting real-world applications for tem- poral data mining are hindered by concept drift. One particular form of concept drift is character-.
Temporal Data Mining in Dynamic Feature Spaces - ACM Digital Library
dl.acm.org › doi › ICDM.2006.157
Dec 18, 2006 · One particular form of concept drift is characterized by changes to the underlying feature space. Seemingly little has been done in this area.
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We propose a framework for rigorous analysis of such state representations in vanishing memory state space models such as echo state networks (ESN).
The shape space representation of time series allows for a definition of a novel similarity measure for time series which we call shape space distance measure.
The key tool for building linear models in the feature space is the inner product. One can grasp workings of a kernel machine by understanding of how the data ...
Temporal analysis and temporal data mining are concerned with extracting useful information from time-oriented data (see Brockwell and Davis, 1991; Antunes and ...
Apr 15, 2021 · Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time.