Spectral methods for growth curve clustering
Snježana Majstorović (),
Kristian Sabo (),
Johannes Jung () and
Matija Klarić ()
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Snježana Majstorović: J.J. Strossmayer University of Osijek
Kristian Sabo: J.J. Strossmayer University of Osijek
Johannes Jung: Technical University of Berlin
Matija Klarić: J.J. Strossmayer University of Osijek
Central European Journal of Operations Research, 2018, vol. 26, issue 3, 715-737
Abstract The growth curve clustering problem is analyzed and its connection with the spectral relaxation method is described. For a given set of growth curves and similarity function, a similarity matrix is defined, from which the corresponding similarity graph is constructed. It is shown that a nearly optimal growth curve partition can be obtained from the eigendecomposition of a specific matrix associated with a similarity graph. The results are illustrated and analyzed on the set of synthetically generated growth curves. One real-world problem is also given.
Keywords: Curve clustering; Similarity graph; Laplacian matrix; Modularity matrix; Spectral methods (search for similar items in EconPapers)
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