Consensus clustering using kNN mode seeking

4Citations
Citations of this article
9Readers
Mendeley users who have this article in their library.

This article is free to access.

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.

Cite

CITATION STYLE

APA

Myhre, J. N., Mikalsen, K. Ø., Løkse, S., & Jenssen, R. (2015). Consensus clustering using kNN mode seeking. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9127, pp. 175–186). Springer Verlag. https://doi.org/10.1007/978-3-319-19665-7_15

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free