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Robust clustering using a kNN mode seeking ensemble

Permanent link
https://hdl.handle.net/10037/13743
DOI
https://doi.org/10.1016/j.patcog.2017.11.023
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Accepted manuscript version (PDF)
Date
2017-12-02
Type
Journal article
Tidsskriftartikkel
Peer reviewed

Author
Myhre, Jonas Nordhaug; Mikalsen, Karl Øyvind; Løkse, Sigurd; Jenssen, Robert
Abstract
In this paper we present a new algorithm for parameter-free clustering by mode seeking. Mode seeking, especially in the form of the mean shift algorithm, is a widely used strategy for clustering data, but at the same time prone to poor performance if the parameters are not chosen correctly. We propose to form a clustering ensemble consisting of repeated and bootstrapped runs of the recent kNN mode seeking algorithm, an algorithm which is faster than ordinary mean shift and more suited for high dimensional data. This creates a robust mode seeking clustering algorithm with respect to the choice of parameters and high dimensional input spaces, while at the same inheriting all other strengths of mode seeking in general. We demonstrate promising results on a number of synthetic and real data sets.
Description
Accepted manuscript version. Published version available at https://doi.org/10.1016/j.patcog.2017.11.023. Accepted manuscript version, licensed CC BY-NC-ND 4.0.
Publisher
Elsevier
Citation
Myhre, J.N., Mikalsen, K.Ø., Løkse, S. & Jenssen, R. (2017). Robust clustering using a kNN mode seeking ensemble. Pattern Recognition, 76, 491-505. https://doi.org/10.1016/j.patcog.2017.11.023
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