Abstract
This paper presents an efficient clustering technique which can identify any embedded and nested cluster over any variable density space. The proposed algorithm is basically an enhanced version of DBSCAN [4] and OPTICS [7]. Experimental results are reported to establish that the proposed clustering technique outperforms both DBSCAN and OPTICS in terms of complex cluster detection.
Keywords: Variable density, embedded cluster, core-distance, cluster, core neighborhood, unsupervised.
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© 2005 Springer-Verlag Berlin Heidelberg
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Roy, S., Bhattacharyya, D.K. (2005). An Approach to Find Embedded Clusters Using Density Based Techniques. In: Chakraborty, G. (eds) Distributed Computing and Internet Technology. ICDCIT 2005. Lecture Notes in Computer Science, vol 3816. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11604655_59
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DOI: https://doi.org/10.1007/11604655_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30999-4
Online ISBN: 978-3-540-32429-4
eBook Packages: Computer ScienceComputer Science (R0)