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A distance-type-insensitive clustering approach

Gu, Xiaowei, Angelov, Plamen, Zhao, Zhijin (2019) A distance-type-insensitive clustering approach. Applied Soft Computing, 77 . pp. 622-634. ISSN 1568-4946. (doi:10.1016/j.asoc.2019.01.028) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90198)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1016/j.asoc.2019.01.028

Abstract

In this paper, we offer a method aiming to minimize the role of distance metric used in clustering. It is well known that distance metrics used in clustering algorithms heavily influence the end results and also make the algorithms sensitive to imbalanced attribute/feature scales. To solve these problems, a new clustering algorithm using a per-attribute/feature ranking operating mechanism is proposed in this paper. Ranking is a rarely used discrete, nonlinear operator by other clustering algorithms. However, it also has unique advantages over the dominantly used continuous operators. The proposed algorithm is based on the ranks of the data samples in terms of their spatial separation and is able to provide a more objective clustering result compared with the alternative approaches. Numerical examples on benchmark datasets prove the validity and effectiveness of the proposed concept and principles.

Item Type: Article
DOI/Identification number: 10.1016/j.asoc.2019.01.028
Uncontrolled keywords: Clustering; Distance metric; Ranking; Spatial separation
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 14 Sep 2021 11:23 UTC
Last Modified: 15 Sep 2021 16:10 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90198 (The current URI for this page, for reference purposes)
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