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An Effective Delaunay Based Min_Max Cp-Tree Algorithm for Spatial Data Analysis

The need to find the best-fit arguments in spatial co-location mining is not the only concern, the other being the manipulation of data to find arguments and this opposes the philosophy of ''let the data speak for themselves'' that underpins exploratory data analysis (Vladimir Estivill-Castro and Ickjai Lee ,2000). In spatial co-location pattern mining, the neighbourhood is a major challenge and plays a key part. The Voronoi diagram has been proposed as an alternative way to model neighbourhood amongst points, overcome the limitations of conventional data models, first it represents topology spatial adjacency explicitly. If two points share a voronoi edge, then they are treated as neighbours. This adjacency information is so fundamental that it constitutes the dual known as the delaunay diagram. Delaunay diagram (Vladimir Estivill-Castro and Ickjai Lee ,2000) possesses a number of unique properties, which can be successfully used for spatial neighbourhood. In this work, the delaunay diagram is applied to model the neighbourhood. The Interesting measure (Shashi Shekhar et al, 2003) used in classical data mining may not be the most suitable one in a spatial context. Therefore, spatial co-location mining requires good measures. The challenges lie in selection of interesting measure like minimum prevalent threshold of co-location. If the prevalence threshold is set too high, there may be only a small number of result sets or even effect less. If the threshold is too low, too many result patterns can be generated with an exceedingly long computational time. They make an analysis of the discovered patterns impractical and even vain. The new interesting measures such as Membership ratio, Minimum membership ratio and Max_Confidence have been proposed to find the useful co-location patterns. Using these new measures, a novel efficient Min-Max based Cp-Tree Co-location algorithm is developed to analyze the precise co-location patterns from the complex spatial data to tide over the limitations of the existing co-location approach. Finally, the performance of proposed approaches with existent approaches have been presented and discussed.