An improved algorithm of a priori based on geostatistics
Jiangping Chen, Rong Wang, Xuehua Tang · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2008
In data mining one of the classical algorithms is Apriori which has been developed for association rule mining in large transaction database. And it cannot been directly used in spatial association rules mining. The main difference between data mining in relational DB and in spatial DB is that attributes of the neighbors of some object of interest may have an influence on the object and therefore have to be considered as well. The explicit location and extension of spatial objects define implicit relations of spatial neighborhood (such as topological, distance and direction relations) which are used by spatial data mining algorithms. Therefore, new techniques are required for effective and efficient spatial data mining. Geostatistics are statistical methods used to describe spatial relationships among sample data and to apply this analysis to the prediction of spatial and temporal phenomena. They are used to explain spatial patterns and to interpolate values at unsampled locations. This paper put forward an improved algorithm of Apriori about mining association rules with geostatistics. First the spatial autocorrelation of the attributes with location were estimated with the geostatistics methods such as kriging and Spatial Autoregressive Model (SAR). Then a spatial autocorrelation model of the attributes were built. Later an improved algorithm of apriori combined with the spatial autocorrelation model were offered to mine the spatial association rules. Last an experiment of the new algorithm were carried out on the hayfever incidence and climate factors in UK. The result shows that the output rules is matched with the references.