An improved design approach in spatial databases using frequent Association Rule Mining algorithm

Recently Negative Association Rule Mining (NARM) has become a focus in the field of spatial data mining. Negative association rules are useful in data analysis to identify objects that conflict with each other or that complement each other. Much effort has been devoted for developing algorithms for efficiently discovering relation between objects in space. All the traditional association rule mining algorithms were developed to find positive associations between objects. By positive correlation we refer to associations between frequently occurring objects in space such as a city is always located near a river and so on. Recently the problem of identifying negative associations (or ¿dissociations¿) that is absence of objects has been explored and considered relevant. This paper presents an improved design approach for mining both positive and negative association rules in spatial databases. This approach extends traditional association rules to include negative association rules using a minimum support count. Experimental results show that this approach is efficient on simple and sparse datasets when minimum support is high to some degree, and it overcomes some limitations of the previous mining methods. The proposed form will extend related applications of negative association rules to a greater extent.