Abstract
Border points are those instances located at the outer margin of dense clusters of samples. The detection is important in many areas such as data mining, image processing, robotics, geographic information systems and pattern recognition. In this paper we propose a novel method to detect border samples. The proposed method makes use of a discretization and works on partitions of the set of points. Then the border samples are detected by applying an algorithm similar to the presented in reference [8] on the sides of convex hulls. We apply the novel algorithm on classification task of data mining; experimental results show the effectiveness of our method.
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References
Edelsbrunner, H., Mücke, E.P.: Three-dimensional alpha shapes. ACM Trans. Graph. 13(1), 43–72 (1994)
Bader, M.A., Sablatnig, M., Simo, R., Benet, J., Novak, G., Blanes, G.: Embedded real-time ball detection unit for the yabiro biped robot. In: 2006 International Workshop on Intelligent Solutions in Embedded Systems (June 2006)
Zhang, J., Kasturi, R.: Weighted boundary points for shape analysis. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. 1598–1601 (August 2010)
Hoogs, A., Collins, R.: Object boundary detection in images using a semantic ontology. In: Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2006, p. 111 (June 2006)
Edelsbrunner, H., Kirkpatrick, D., Seidel, R.: On the shape of a set of points in the plane. IEEE Transactions on Information Theory 29(4), 551–559 (1983)
Galton, A., Duckham, M.: What is the Region Occupied by a Set of Points? In: Raubal, M., Miller, H.J., Frank, A.U., Goodchild, M.F. (eds.) GIScience 2006. LNCS, vol. 4197, pp. 81–98. Springer, Heidelberg (2006)
Xia, C., Hsu, W., Lee, M., Ooi, B.: Border: efficient computation of boundary points. IEEE Transactions on Knowledge and Data Engineering 18(3), 289–303 (2006)
Moreira, J.C.A., Santos, M.Y.: Concave hull: A k-nearest neighbours approach for the computation of the region occupied by a set of points. In: GRAPP (GM/R), pp. 61–68 (2007), http://dblp.uni-trier.de
de Berg, M., van Kreveld, M., Overmars, M., Schwarzkopf, O.: Computational Geometry: Algorithms and Applications, 3rd edn. Springer, Heidelberg (2008)
O’Rourke, J.: Computational Geometry in C. Cambridge University Press (1998), hardback ISBN: 0521640105; Paperback: ISBN 0521649765, http://maven.smith.edu/~orourke/books/compgeom.html
Noble, B., Daniel, J.W.: Applied Linear Algebra, 3rd edn. (1988)
Yu, W., Li, X.: On-line fuzzy modeling via clustering and support vector machines. Information Sciences 178, 4264–4279 (2008)
Ho, T., Kleinberg, E.: Checkerboard data set (1996), http://www.cs.wisc.edu/math-prog/mpml.html
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López Chau, A., Li, X., Yu, W., Cervantes, J., Mejía-Álvarez, P. (2011). Border Samples Detection for Data Mining Applications Using Non Convex Hulls. In: Batyrshin, I., Sidorov, G. (eds) Advances in Soft Computing. MICAI 2011. Lecture Notes in Computer Science(), vol 7095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25330-0_23
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DOI: https://doi.org/10.1007/978-3-642-25330-0_23
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