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rather than the [[Laplacian_matrix#Symmetric_normalized_Laplacian|symmetric normalized Laplacian]] matrix.
Partitioning may be done in various ways, such as by computing the median <math>m</math> of the components of the eigenvalue{{which|date=October 2014}} <math>v</math>, and placing all points whose component in <math>v</math> is greater than <math>m</math> in <math>B_1</math>, and the rest in <math>B_2</math>. The algorithm can be used for hierarchical clustering by repeatedly partitioning the subsets in this fashion.
Alternatively to computing just one eigenvector, ''k'' [[eigenvector]]s for some ''k'', are computed, and then another algorithm (e.g. [[k-means clustering]]) is used to cluster points by their respective ''k'' components in these eigenvectors.
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