Hierarchical Window Centric Method of Modeling Spatial Co-Location Patterns on Spatial Database

With the growing technology, most of the focus is on spatial data mining. Spatial data mining refers to the extraction of unknown and unexpected information from spatial data sets of massive, high dimensionality and complex spatial databases. Spatial data mining is a process to discover related knowledge, potentially constructive and high utility patterns embedded in geographic Information. Application specific tools for extracting efficient and useful information from spatial data sets can be of great importance to the organizations which own, generate and manage large databases. The objective of co-location pattern mining is to find the subset of features frequently located together in the same region. Three modeling methods for co-location patterns[16,19] are event centric model, feature centric model and window centric model. We have proposed a new method for modeling co-location patterns “Hierarchical Window Centric Model”, which is an extension of Window Centric Model. We would carry out experimental evaluations and performance tuning in the near future.