Enhancing spatial association rule mining in geographic databases

Association rule mining algorithms generate huge amounts of patterns. In spatial association rule mining this problem increases because a significant amount of associations is well known a priori. This paper presents a novel approach for mining spatial association rules, using background knowledge. The main contributions include the use of geographic database schemas and geo-ontologies for (i) the improvement of geographic data pre-processing, (ii) the elimination of well known patterns and (iii) the generation of maximal frequent patterns without redundant and non-interesting associations.

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