Structure Across Scales: Hierarchical Decomposition of Spatiotemporal Data Using A Scale-Space Approach
نویسنده
چکیده
The ability to derive relationships between parts (granules) of a phenomenon that extends over space and time at different scales is essential in numerous application areas. Currently, the task of deriving a hierarchical decomposition of a spatiotemporal phenomenon relies on expert domain knowledge and is driven by a human operator. With the increased availability of spatiotemporal data this approach is quickly becoming impractical, thus requiring the development of automated tools for spatiotemporal data mining over multiple granularities. This presentation will review the problem of partitioning a spatiotemporal phenomenon into salient granular parts over multiple scales, and will introduce a novel analytical approach for reconstructing a multi-scale hierarchical decomposition of a given spatiotemporal data set.
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