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  • TasoulisPR

    Rights statement: This is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition, 107, 2020 DOI: 10.1016/j.patcog.2020.107508

    Accepted author manuscript, 1.53 MB, PDF document

    Embargo ends: 19/06/21

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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Nonlinear Dimensionality Reduction for Clustering

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Article number107508
<mark>Journal publication date</mark>1/11/2020
<mark>Journal</mark>Pattern Recognition
Volume107
Number of pages11
Publication StatusPublished
Early online date19/06/20
<mark>Original language</mark>English

Abstract

We introduce an approach to divisive hierarchical clustering that is capable of identifying clusters in nonlinear manifolds. This approach uses the isometric mapping (Isomap) to recursively embed (subsets of) the data in one dimension, and then performs a binary partition designed to avoid the splitting of clusters. We provide a theoretical analysis of the conditions under which contiguous and high density clusters in the original space are guaranteed to be separable in the one dimensional embedding. To the best of our knowledge there is little prior work that studies this problem. Extensive experiments on simulated and real data sets show that hierarchical divisive clustering algorithms derived from this approach are effective.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Pattern Recognition. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Pattern Recognition, 107, 2020 DOI: 10.1016/j.patcog.2020.107508