Nonlinear Dimensionality Reduction for Clustering

Tasoulis, Sotiris and Pavlidis, Nicos and Roos, Teemu (2020) Nonlinear Dimensionality Reduction for Clustering. Pattern Recognition, 107: 107508. ISSN 0031-3203

[thumbnail of TasoulisPR]
Text (TasoulisPR)
TasoulisPR.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial-NoDerivs.

Download (1MB)


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.

Item Type:
Journal Article
Journal or Publication Title:
Pattern Recognition
Additional Information:
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
Uncontrolled Keywords:
?? nonlinearitydimensionality reductiondivisive hierarchical clusteringmanifold clusteringartificial intelligencesignal processingsoftwarecomputer vision and pattern recognition ??
ID Code:
Deposited By:
Deposited On:
19 Jun 2020 08:25
Last Modified:
14 Dec 2023 01:42