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
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
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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