Abstract
In order to detect boundary points of clusters effectively, we propose a technique making use of a point’s distribution feature of its Eps neighborhood to detect boundary points, and develop a boundary points detecting algorithm BRIM (an efficient Boundary points detecting algorithm). Experimental results show that BRIM can detect boundary points in noisy datasets containing clusters of different shapes and sizes effectively and efficiently.
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© 2007 Springer Berlin Heidelberg
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Qiu, BZ., Yue, F., Shen, JY. (2007). BRIM: An Efficient Boundary Points Detecting Algorithm. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_83
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DOI: https://doi.org/10.1007/978-3-540-71701-0_83
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71700-3
Online ISBN: 978-3-540-71701-0
eBook Packages: Computer ScienceComputer Science (R0)