Enhanced spatial mining algorithm using fuzzy quadtrees

Abstract

Spatial Mining differs from regular data mining in parallel with the difference in spatial and non-spatial data. The attributes of a spatial object is influenced by the attributes of the spatial object and moreover by the spatial location. A new algorithm is proposed for spatial mining by applying an image extraction method on hierarchical Quad tree spatial data structure. Homogeneity of the grid is the entropy measure which decides the further subdivision of the quadrant. The decision for decomposition to further sub quadrants is based on fuzzy rules generated using the statistical measures mean and standard deviation of the region. Finally, the algorithm proceeds by applying low level image extraction on domain dense nodes of the quad tree. © 2011 Springer-Verlag.