Reducing uninteresting spatial association rules in geographic databases using background knowledge: a summary of results

Many association rule-mining algorithms have been proposed in the last few years. Their main drawback is the huge amount of generated patterns. In spatial association rule mining, besides the large amount of rules, many are well-known geographic domain associations explicitly represented in geographic database schemas. Existing algorithms have only considered the data, while the schema has not been considered. The result is that also the associations explicitly represented in geographic database schemas are extracted by association rule-mining algorithms. With the aim to reduce the number of well-known patterns and association rules, this paper presents a summary of results of a novel approach to extract patterns from geographic databases. A two step-pruning method is presented to avoid the generation of association rules that are previously known to be uninteresting. Experiments with real geographic databases show a considerable time reduction in both geographic data pre-processing and spatial association rule mining, with a very significant reduction in the total number of rules.