A Method for Autonomous Data Partitioning

Gu, Xiaowei and Angelov, Plamen Parvanov and Principe, Jose (2018) A Method for Autonomous Data Partitioning. Information Sciences, 460-46. pp. 65-82. ISSN 0020-0255

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Abstract

In this paper, we propose a fully autonomous, non-parametric, data partitioning algorithm, which is able to automatically recognize local maxima of the density from empirical observations and use them as the focal points to form shape-free data clouds, i.e. a form of Voronoi tessellation. It 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:

Journal Article

Journal or Publication Title:

Information Sciences

Additional Information:

This is the author’s version of a work that was accepted for publication in Information Sciences. 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 Information Sciences, 460-461, 2018 DOI: 10.1016/j.ins.2018.05.030

Uncontrolled Keywords:

/dk/atira/pure/subjectarea/asjc/1700/1702

Subjects:

?? autonomousdata partitioninglocal modesvoronoi tessellationclustering-algorithmmean shiftrecognitionclassificationartificial intelligencetheoretical computer sciencesoftwareinformation systems and managementcontrol and systems engineeringcomputer science a ??

Departments:

Faculty of Science and Technology > School of Computing & Communications

ID Code:

125127

Deposited By:

ep_importer_pure

Deposited On:

14 May 2018 08:24

Refereed?:

Yes

Published?:

Published

Last Modified:

12 Oct 2024 00:09

URI:

https://eprints.lancs.ac.uk/id/eprint/125127