Mining Spatio-temporal Data at Different Levels of Detail
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Title: | Mining Spatio-temporal Data at Different Levels of Detail | Authors: | Camossi, Elena Bertolotto, Michela Kechadi, Tahar |
Permanent link: | http://hdl.handle.net/10197/1442 | Date: | 8-May-2008 | Online since: | 2009-09-28T14:22:56Z | Abstract: | In this paper we propose a methodology for mining very large spatio-temporal datasets. We propose a two-pass strategy for mining and manipulating spatio-temporal datasets at different levels of detail (i.e., granularities). The approach takes advantage of the multi-granular capability of the underlying spatio-temporal model to reduce the amount of data that can be accessed initially. The approach is implemented and applied to real-world spatio-temporal datasets. We show that the technique can deal easily with very large datasets without losing the accuracy of the extracted patterns, as demonstrated in the experimental results. | Funding Details: | Science Foundation Ireland; Irish Research Council for Science, Engineering & Technology | Type of material: | Conference Publication | Publisher: | Springer-Verlag | Copyright (published version): | Springer 2008 | Keywords: | Spatio-temporal data mining; Spatio-temporal multi-granularity | Subject LCSH: | Data mining Granular computing |
DOI: | 10.1007/978-3-540-78946-8_12 | Other versions: | http://dx.doi.org/10.1007/978-3-540-78946-8_12 | Language: | en | Status of Item: | Peer reviewed | Is part of: | Bernard, L., Friis-Christen, A., and Pundt, H. (eds.) The European Information Society : taking geo-information science one step further | Conference Details: | Presented at the 11th AGILE International Conference on Geographic Information Science (AGILE 2008), Girona, Spain, 5-8 May 2008 | ISBN: | 978-3-540-78945-1 |
Appears in Collections: | Computer Science Research Collection |
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