Abstract
Spatial data clustering groups similar objects based on their distance, connectivity, or their relative density in space whereas in the real world, there exist many physical constraints e.g. highways, rivers, hills etc. that may affect the result of clustering. Therefore, these obstacles when taken into consideration render the cluster analysis a hopelessly slow exercise. In this paper, a clustering method is being proposed that considers the presence of physical obstacles and uses obstacle modeling as a preprocessing step. With a view to prune the search space and reduce the complexity at search levels, the work further incorporates the hierarchical structure into the existing clustering structure. The clustering algorithm can detect clusters of arbitrary shapes and sizes and is insensitive to noise and input order.
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Duhan, N., Sharma, A.K. (2011). DBCCOM: Density Based Clustering with Constraints and Obstacle Modeling. In: Aluru, S., et al. Contemporary Computing. IC3 2011. Communications in Computer and Information Science, vol 168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22606-9_24
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DOI: https://doi.org/10.1007/978-3-642-22606-9_24
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