Abstract
The problem of clustering data has been driven by a demand from various disciplines engaged in exploratory data analysis, such as medicine taxonomy, customer relationship management and so on. However, Most of the algorithms designed to handle data in the form of point clouds fail to cluster data that expose a manifold structure. The high dimensional data sets often exhibit geometrical structures which are often important in clustering data on manifold. Motivated by the fact, we believe that a good similarity measure on a manifold should reflect not only the statistical properties but also the geometrical properties of given data. We model the similarity between data points in statistical and geometrical perspectives, then a modified version of spectral algorithm on manifold is proposed to reveal the structure. The encouraging results on several artificial and real-world data set are obtained which validate our proposed clustering algorithm.
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Cheng, Y., Tong, Q. (2010). Spectral Clustering on Manifolds with Statistical and Geometrical Similarity. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_54
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DOI: https://doi.org/10.1007/978-3-642-13278-0_54
Publisher Name: Springer, Berlin, Heidelberg
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