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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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -