Mining Spatio-temporal Data at Different Levels of Detail
|Title:||Mining Spatio-temporal Data at Different Levels of Detail||Authors:||Camossi, Elena
|Permanent link:||http://hdl.handle.net/10197/1442||Date:||8-May-2008||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
|DOI:||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|
|Appears in Collections:||Computer Science Research Collection|
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