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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

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A Method for Autonomous Data Partitioning. / Gu, Xiaowei; Angelov, Plamen Parvanov; Principe, Jose .

In: Information Sciences, Vol. 460-461, 09.2018, p. 65-82.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Gu, X, Angelov, PP & Principe, J 2018, 'A Method for Autonomous Data Partitioning', Information Sciences, vol. 460-461, pp. 65-82. https://doi.org/10.1016/j.ins.2018.05.030

APA

Vancouver

Author

Gu, Xiaowei ; Angelov, Plamen Parvanov ; Principe, Jose . / A Method for Autonomous Data Partitioning. In: Information Sciences. 2018 ; Vol. 460-461. pp. 65-82.

Bibtex

@article{c591d0e2dce24fb6b366e1a60abdf5f5,
title = "A Method for Autonomous Data Partitioning",
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.",
keywords = "Autonomous, Data partitioning, Local modes, Voronoi tessellation, CLUSTERING-ALGORITHM, MEAN SHIFT, RECOGNITION, CLASSIFICATION",
author = "Xiaowei Gu and Angelov, {Plamen Parvanov} and Jose Principe",
note = "This is the author{\textquoteright}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",
year = "2018",
month = sep,
doi = "10.1016/j.ins.2018.05.030",
language = "English",
volume = "460-461",
pages = "65--82",
journal = "Information Sciences",
issn = "0020-0255",
publisher = "Elsevier Inc.",

}

RIS

TY - JOUR

T1 - A Method for Autonomous Data Partitioning

AU - Gu, Xiaowei

AU - Angelov, Plamen Parvanov

AU - Principe, Jose

N1 - 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

PY - 2018/9

Y1 - 2018/9

N2 - 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.

AB - 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.

KW - Autonomous

KW - Data partitioning

KW - Local modes

KW - Voronoi tessellation

KW - CLUSTERING-ALGORITHM

KW - MEAN SHIFT

KW - RECOGNITION

KW - CLASSIFICATION

U2 - 10.1016/j.ins.2018.05.030

DO - 10.1016/j.ins.2018.05.030

M3 - Journal article

VL - 460-461

SP - 65

EP - 82

JO - Information Sciences

JF - Information Sciences

SN - 0020-0255

ER -