Abstract
This paper presents a distributed Grid-based Density Clustering using Triangle-subdivision (DGDCT), capable of identifying arbitrary shaped embedded clusters as well as multi density clusters over large spatial datasets. Experimental results are presented to establish the superiority of the technique in terms of scale-up, speedup as well as cluster quality.
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Sarmah, S., Das, R., Bhattacharyya, D.K. (2007). DGDCT: A Distributed Grid-Density Based Algorithm for Intrinsic Cluster Detection over Massive Spatial Data. In: Rao, S., Chatterjee, M., Jayanti, P., Murthy, C.S.R., Saha, S.K. (eds) Distributed Computing and Networking. ICDCN 2008. Lecture Notes in Computer Science, vol 4904. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77444-0_22
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DOI: https://doi.org/10.1007/978-3-540-77444-0_22
Publisher Name: Springer, Berlin, Heidelberg
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