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5 novembre 2014
- handle: 10670/1.1fudu0
- hal: hal-01405553
- doi: 10.1007/978-3-662-45237-0_6
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info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-662-45237-0_6
http://creativecommons.org/licenses/by/ , info:eu-repo/semantics/OpenAccess
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spectral clustering nearest neighbor graphSujets proches
Natural science Sciences Science of science AlgorismCiter ce document
Małgorzata Lucińska et al., « Spectral Clustering Based on Analysis of Eigenvector Properties », HAL-SHS : sciences de l'information, de la communication et des bibliothèques, ID : 10.1007/978-3-662-45237-0_6
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In this paper we propose a new method for choosing the number of clusters and the most appropriate eigenvectors, that allow to obtain the optimal clustering. To accomplish the task we suggest to examine carefully properties of adjacency matrix eigenvectors: their weak localization as well as the sign of their values. The algorithm has only one parameter — the number of mutual neighbors. We compare our method to several clustering solutions using different types of datasets. The experiments demonstrate that our method outperforms in most cases many other clustering algorithms.