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A method for autonomous data partitioning

Gu, Xiaowei, Angelov, Plamen P., Príncipe, José C. (2018) A method for autonomous data partitioning. Information Sciences, 460-46 . pp. 65-82. ISSN 0020-0255. (doi:10.1016/j.ins.2018.05.030) (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:90202)

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.ins.2018.05.030

Abstract

In this paper, we propose a fully autonomous, local-modes-based data partitioning algorithm, which is able to automatically recognize local maxima of the data density from empirical observations and use them as focal points to form shape-free data clouds, i.e. a form of Voronoi tessellation. The method is free from user- and problem- specific parameters and prior assumptions. The proposed algorithm has two versions: i) offline for static data and ii) evolving for streaming data. Numerical results based on benchmark datasets prove the validity of the proposed algorithm and demonstrate its excellent performance and high computational efficiency compared with the state-of-art clustering algorithms.

Item Type: Article
DOI/Identification number: 10.1016/j.ins.2018.05.030
Uncontrolled keywords: Autonomous; Data partitioning; Local modes; Voronoi tessellation
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 12:52 UTC
Last Modified: 15 Sep 2021 15:53 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90202 (The current URI for this page, for reference purposes)
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