New Geometrical Similarity-based Clustering Algorithm for GIS Vector Data

Abstract Geographic Information System(GIS) are usually classified into raster, vector, and raster -vector systems. The research deals with proposing new graph mining algorithm called GIS-GMA. The algorithm is used for clustering the vector features of GIS. The vector data are usually stored in data files called shape files. These files contains the (point, lines, polygons,...,etc). The extracted data is then stored in a dataset to be processed by the proposed algorithm to discover the full and partial similarities among map objects to assist the clustering and analysis of map data. It deals with clustering the polylines and polygonal data. The research results lead to build GIS prototype with spatial data mining facilities to cluster GIS vector data and giving fine clustering results,it is implemented using MicroSoft VS-2005 and ESRI ArcObjects