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Consensus Clustering Using kNN Mode Seeking

  • Jonas Nordhaug MyhreEmail author
  • Karl Øyvind Mikalsen
  • Sigurd Løkse
  • Robert Jenssen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9127)

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.

Keywords

Kernel Density Estimate Spectral Cluster Cluster Scheme True Label Consensus Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jonas Nordhaug Myhre
    • 1
    Email author
  • Karl Øyvind Mikalsen
    • 1
  • Sigurd Løkse
    • 1
  • Robert Jenssen
    • 1
  1. 1.Machine Learning @ UiT LabUiT - The Arctic University of NorwayTromsøNorway

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