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Geographical Decision Support System (Geo-DSS) is a demanding field, since enormous amount of spatial data have been collected in various applications, ranging form Remote Sensing to GIS, Computer Cartography, Environmental Assessment and Planning. Although some efforts were made to combine spatial mining with Spatial Decision Support System but mostly researchers for spatial database are using a popular data mining approach-Apriori based association rule mining. There are two major limitations in existing approaches; the biggest being, that in a typical Apriori based spatial association the same records are required to be scanned again and again to find out the frequent sets. This becomes cumbersome, as spatial data is already known to be large in size. As far as sparse data is concerned, an Apriori based spatial association rule may even be considered but when there is dense data there were other approaches giving better performance. Researchers discuss only the positive spatial association rules; they have not considered the spatial negative association rules. Negative association rules are very useful in some spatial problems and are capable of extracting some useful and previously unknown hidden information. As this approach makes computation faster, it is thus better candidate for integration into Geo-DSS architectural framework. We have tried to design a particular Decision support system using spatial positive and negative association rule with efficient P-Tree and T-Tree

Exploring Spatial ARM (Spatial Association Rule Mining) for Geo-Decision Support System