Title :
A Heuristic for the Automatic Parametrization of the Spectral Clustering Algorithm
Author :
Bruneau, P. ; Parisot, O. ; Otjacques, B.
Author_Institution :
Centre de Rech. Public - Gabriel Lippmann, Belvaux, Luxembourg
Abstract :
Finding the optimal number of groups in the context of a clustering algorithm is identified as a difficult problem. In this article, we automate this choice for the spectral clustering algorithm with a novel heuristic. Our method is deterministic, and remarkable by its low computational burden. We show its effectiveness with respect to the state of the art, and further investigate assumptions underlying previous work through an empirical study, with the support of synthetic and real data sets.
Keywords :
data mining; learning (artificial intelligence); pattern clustering; automatic parametrization; data mining; machine learning; real data sets; semi-supervised learning; spectral clustering algorithm; synthetic data sets; Clustering algorithms; Eigenvalues and eigenfunctions; Equations; Indexes; Iris; Laplace equations; Principal component analysis; Classification and clustering; Machine learning and data mining; Semi-supervised learning and spectral methods;
Conference_Titel :
Pattern Recognition (ICPR), 2014 22nd International Conference on
Conference_Location :
Stockholm
DOI :
10.1109/ICPR.2014.235
