الفهرس 
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Abstract
With the large amount of data stored in spatial databases, one may like to find groups of data, which share similar features. Thus spatial clustering is an important component of spatial data mining, and it has become an active area of research in data mining. Spatial clustering can be used in many applications geographical, medical, astronomical, or other applications. However, most of the clustering algorithms developed so far ignored the fact that physical obstacles exist in the real world, such as rivers, lakes and highways, and their presence may affect the result of clustering substantially. So far, the problem of spatial clustering in the presence of natural obstacles has been studied in the scope of partitioning-based algorithms and grid-based algorithms. However, for certain data set structures in some applications, there is a need for the density-based notion of a cluster, which can discover natural clusters with arbitrary shapes and are sensitive to the noises and outliers. Thus, this thesis studies the problem of Clustering with Obstacle Entities in density-based algorithms, and develops a density-based algorithm COE-DBSCAN based on the DBSCAN algorithms, which performs effective clustering by taking obstacle entities into consideration. In addition, the complexity of the proposed algorithm has been analyzed and its results are compared with previous work in this field. The thesis is concluded and some further research issues are identified as well