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    Rights statement: 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

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A Method for Autonomous Data Partitioning

Research output: Contribution to journalJournal article

Published
<mark>Journal publication date</mark>09/2018
<mark>Journal</mark>Information Sciences
Volume460-461
Number of pages18
Pages (from-to)65-82
Publication statusPublished
Original languageEnglish

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.

Bibliographic note

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