Enhanced Spatial Mining Algorithm Using Fuzzy Quadtrees

• Published in: Computational Intelligence and Information Technology pp. 110 - 116
• Main Authors: Varghese, Bindiya M., Unnikrishnan, A., Poulose Jacob, K.
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2011
• Series: Communications in Computer and Information Science
• Subjects: Fuzzy Quad Trees; Image Extraction; Spatial Clustering; Spatial Mining
• ISBN: 364225733X; 9783642257339
• ISSN: 1865-0929; 1865-0937
• DOI: 10.1007/978-3-642-25734-6_17

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.