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Consensus Clustering Using kNN Mode Seeking

Permanent link
https://hdl.handle.net/10037/8818
DOI
http://doi.org/10.1007/978-3-319-19665-7_15
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Date
2015-06-09
Type
Chapter
Bokkapittel

Author
Myhre, Jonas Nordhaug; Mikalsen, Karl Øyvind; Løkse, Sigurd; Jenssen, Robert
Abstract
In this paper we present a novel clustering approach which combines two modern strategies, namely consensus clustering, and two stage clustering as represented by the mean shift spectral clustering algorithm. We introduce the recent kNN mode seeking algorithm in the consensus clustering framework, and the information theoretic kNN Cauchy Schwarz divergence as foundation for spectral clustering. In combining these frameworks, two well known problematic issues are directly bypassed; the kernel bandwidth choice of the kernel density based mean shift and the computational complexity of the mean shift iterations. We demonstrate experiments on both real and synthetic data as a proof of concept for our contributions.
Description
Published version. Source at http://doi.org/10.1007/978-3-319-19665-7_15.
Publisher
Springer
Citation
Lecture Notes in Computer Science. 11 p. Springer, 2015
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