Abstract
Clustering aims to partition a data set by bringing together similar elements in subsets. Spectral clustering consists in selecting dominant eigenvectors of a matrix called affinity matrix in order to define a low-dimensional data space in which data points are easy to cluster. The key is to design a good affinity matrix. If we consider the usual Gaussian affinity matrix, it depends on a scaling parameter which is difficult to select. Our goal is to grasp the influence of this parameter and to propose an expression with a reasonable computational cost.
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Mouysset, S., Noailles, J., Ruiz, D. (2008). Using a Global Parameter for Gaussian Affinity Matrices in Spectral Clustering. In: Palma, J.M.L.M., Amestoy, P.R., Daydé, M., Mattoso, M., Lopes, J.C. (eds) High Performance Computing for Computational Science - VECPAR 2008. VECPAR 2008. Lecture Notes in Computer Science, vol 5336. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92859-1_34
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DOI: https://doi.org/10.1007/978-3-540-92859-1_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-92858-4
Online ISBN: 978-3-540-92859-1
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