Information Content Extraction on Quad Trees for Active Spatial Image Clustering

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. In this paper, we propose a new algorithm 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. Finally, the algorithm proceeds by applying low level image extraction on domain dense nodes of the quad tree.