عنوان البحث(Papers / Research Title)
New Geometrical Similarity-based Clustering Algorithm for GIS Vector
الناشر \ المحرر \ الكاتب (Author / Editor / Publisher)
توفيق عبد الخالق عباس الاسدي
Citation Information
توفيق,عبد,الخالق,عباس,الاسدي ,New Geometrical Similarity-based Clustering Algorithm for GIS Vector , Time 20/05/2012 09:46:32 : كلية تكنولوجيا المعلومات
وصف الابستركت (Abstract)
New Geometrical Similarity-based Clustering Algorithm for GIS Vector
الوصف الكامل (Full Abstract)
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.
Keywords :
Graph clustering algorithms, Graph mining, GIS data analysis, Mining GIS data ,Spatial data mining.
1. Introduction:-
During the study of related researches to Spatial clustering which deals with spatial data that is generally organized in the form of a set of points or polygons[6] we have found that mining of GIS spatial data is fertile for more research to be more convenient to GIS applications especially the vector data that deals with GIS graphdata like points, lines, polygons,..., etc .The huge amount of GIS-based applications make it essential to improve graph mining techniques to extract the spatial knowledge embedded in that data to support GIS applications to be installed in new data analysis fields to support geographers and GIS users to investigate the similarity among the spaghetti of map objects, and therefore can understand and analyse the spatial relations like clustering them or classifying them.
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