Consensus clustering is a well studied methodology to find partitions through the combination of different formulations or clustering partitions. Different approaches for dealing with this issue using graph clustering have been proposed. Additionally, strategies to find consensus partitions by using graph-based ensemble algorithms have attracted a good deal of attention lately. A particular class of graph clustering algorithms based on spectral theory, named spectral clustering algorithms, has been successfully used in several clustering applications. However, in spite of this, few ensemble approaches based on spectral theory has been investigated. This paper proposes a consensus clustering algorithm based on spectral clustering. Preliminary results presented in this paper show the good potential of the proposed approach. © 2009 Springer Berlin Heidelberg.
CITATION STYLE
Nascimento, M. C. V., De Toledo, F. M. B., & Carvalho, A. C. P. L. F. (2009). Consensus clustering using spectral theory. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5506 LNCS, pp. 461–468). https://doi.org/10.1007/978-3-642-02490-0_57
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