Rough Sets in Spatio-Temporal Data Mining (0)
| Venue: | International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining, volume 2007 of Lecture |
| Citations: | 4 - 2 self |
BibTeX
@INPROCEEDINGS{Bittner_roughsets,
author = {Thomas Bittner},
title = {Rough Sets in Spatio-Temporal Data Mining},
booktitle = {International Workshop on Temporal, Spatial, and Spatio-Temporal Data Mining, volume 2007 of Lecture},
year = {},
pages = {89--104},
publisher = {Springer-Verlag}
}
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Abstract
. In this paper I define spatio-temporal regions as pairs consisting of a spatial and a temporal component and I define topological relations between them. Using the notion of rough sets I define approximations of spatio-temporal regions and relations between those approximations. Based on relations between approximated spatio-temporal regions configurations of spatio-temporal objects can be characterized even if only approximate descriptions of the objects forming them are available. 1 Introduction Rough set theory [Paw82] provides a way of approximating subsets of a set when the set is equipped with a partition or equivalence relation. Rough sets were extensively used in the context of Data Mining, e.g., [Lin95,LC97]. So far, however, they were used mainly in non spatio-temporal contexts, for example, in order to classify and analyze phenomena, like diseases, given a finite number of observations or symptoms, e.g., [NSR92,BNSNT + 95]. It is the purpose of this paper to apply rough...
