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Rough set spatial data modeling for data mining

✍ Scribed by Theresa Beaubouef; Roy Ladner; Frederick Petry

Publisher: John Wiley and Sons
Year: 2004
Tongue: English
Weight: 130 KB
Volume: 19
Category: Article
ISSN: 0884-8173
DOI: 10.1002/int.20019

No coin nor oath required. For personal study only.

✦ Synopsis

Uncertainty management is necessary for real world applications, especially those used with data mining. The Region Connection Calculus (RCC) and egg-yolk methods have proven useful for the representation of vague regions in spatial data. Rough set theory has been shown to be an effective tool for data mining and for uncertainty management in databases. In this study we use a rough set foundation for expressing topological relationships previously defined for the RCC and egg-yolk methods and show that rough sets can improve on the representation of topological relationships and concepts defined with the other models, which leads to improved mining of spatial data. Finally, we provide an extension of spatial association rule generation that will be able to use rough set-modeled spatial data.

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