Abstract
Clustering results often critically depend on density and similarity, and its complexity often changes along with the augment of sample dimensionality. In this paper, we refer to classical shared nearest neighbor clustering algorithm (SNN), and provide a high-dimensional shared nearest neighbor clustering algorithm (DSNN). This DSNN is evaluated using a freeway traffic data set, and experiment results show that DSNN settles many disadvantages in SNN algorithm, such as outliers, statistic, core points, computation complexity etc, also attains better clustering results on multi-dimensional data set than SNN algorithm.
This work is supported by the National Natural Science Foundation of China (60205007) , Natural Science Foundation of Guangdong Province (031558,04300462), Research Foundation of National Science and Technology Plan Project (2004BA721A02), Research Foundation of Science and Technology Plan Project in Guangdong Province (2003C50118) and Research Foundation of Science and Technology Plan Project in Guangzhou City(2002Z3-E0017).
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Yin, J., Fan, X., Chen, Y., Ren, J. (2005). High-Dimensional Shared Nearest Neighbor Clustering Algorithm. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3614. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11540007_60
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DOI: https://doi.org/10.1007/11540007_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28331-7
Online ISBN: 978-3-540-31828-6
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