Article: Density peaks clustering based on geodetic distance and dynamic neighbourhood Journal: International Journal of Bio-Inspired Computation (IJBIC) 2021 Vol.17 No.1 pp.24 - 33 Abstract: Density peaks clustering algorithm uses Euclidean distance as a measure of similarity between the samples and it can achieve a good clustering effect when processing the manifold datasets. Utilising this feature, we propose a density peaks clustering algorithm based on geodetic distance and dynamic neighbourhood. This new algorithm measures the similarity between the samples by using geodetic distance, and the number of neighbours K is dynamically adjusted according to the spatial distribution of samples for geodetic distance computation. By choosing geodetic distance as the similarity measure, the problems of manifold dataset clustering can be easily solved, and the clustering is made more effective when the sparse clusters and dense clusters co-exist. The new algorithm was then compared against the other five clustering algorithms on six synthetic datasets and ten real-world datasets. The experiments showed that the proposed algorithm not only outperformed the other conventional algorithms on manifold datasets, but also achieved a very good clustering effect on multi-scale, cluttered and intertwined datasets. Inderscience Publishers - linking academia, business and industry through research

Title: Density peaks clustering based on geodetic distance and dynamic neighbourhood

Authors: Li Lv; Jiayuan Wang; Runxiu Wu; Hui Wang; Ivan Lee

Addresses: School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China ' School of Information Engineering, Nanchang Institute of Technology, Nanchang, 330099, China ' UniSA STEM, University of South Australia, Adelaide, SA 5000, Australia

Abstract: Density peaks clustering algorithm uses Euclidean distance as a measure of similarity between the samples and it can achieve a good clustering effect when processing the manifold datasets. Utilising this feature, we propose a density peaks clustering algorithm based on geodetic distance and dynamic neighbourhood. This new algorithm measures the similarity between the samples by using geodetic distance, and the number of neighbours K is dynamically adjusted according to the spatial distribution of samples for geodetic distance computation. By choosing geodetic distance as the similarity measure, the problems of manifold dataset clustering can be easily solved, and the clustering is made more effective when the sparse clusters and dense clusters co-exist. The new algorithm was then compared against the other five clustering algorithms on six synthetic datasets and ten real-world datasets. The experiments showed that the proposed algorithm not only outperformed the other conventional algorithms on manifold datasets, but also achieved a very good clustering effect on multi-scale, cluttered and intertwined datasets.

Keywords: density peaks; clustering; geodetic distance; dynamic neighbourhood.

DOI: 10.1504/IJBIC.2021.113363

International Journal of Bio-Inspired Computation, 2021 Vol.17 No.1, pp.24 - 33

Received: 24 Jun 2020
Accepted: 20 Aug 2020
Published online: 01 Mar 2021 *

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Title: Density peaks clustering based on geodetic distance and dynamic neighbourhood

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