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Optimization of Spatial Association Rule Mining using Hybrid Evolutionary Algorithm
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 1 - Number 19 |
| Year of Publication: 2010 |
| Authors: J. Arunadevi, V. Rajamani |
10.5120/397-592
|
J. Arunadevi, V. Rajamani . Optimization of Spatial Association Rule Mining using Hybrid Evolutionary Algorithm. International Journal of Computer Applications. 1, 19 ( February 2010), 86-89. DOI=10.5120/397-592
Abstract
Spatial data refer to any data about objects that occupy real physical space. Attributes within spatial databases usually include spatial information. Spatial data refers to the numerical or categorical values of a function at different spatial locations. Spatial metadata refers to the descriptions of the spatial configuration. Application of classical association rule mining concepts to spatial databases is promising but very challenging. Spatial Association Rule Mining requires new approaches compared to classical association rule mining. Spatial data consists of dependent events compared to transactional data which consist of independent transactions. It is more difficult to classify a discovered spatial association rule as interesting. Instead of much generalized rule more specific rule discovery needs further research.
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