Camossi, ElenaElenaCamossiBertolotto, MichelaMichelaBertolottoKechadi, TaharTaharKechadi2009-09-282009-09-28Springer 22008-05-08978-3-540-78945-1http://hdl.handle.net/10197/1442Presented at the 11th AGILE International Conference on Geographic Information Science (AGILE 2008), Girona, Spain, 5-8 May 2008In 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.2571616 bytesapplication/pdfenSpatio-temporal data miningSpatio-temporal multi-granularityData miningGranular computingConference Publication10.1007/978-3-540-78946-8_12https://creativecommons.org/licenses/by-nc-sa/1.0/