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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, 423, 2018 DOI: 10.1016/j.ins.2017.09.025

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Self-organised direction aware data partitioning algorithm

Research output: Contribution to journalJournal article

Published

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Self-organised direction aware data partitioning algorithm. / Gu, Xiaowei; Angelov, Plamen Parvanov; Kangin, Dmitry; Principe, Jose .

In: Information Sciences, Vol. 423, 01.2018, p. 80-95.

Research output: Contribution to journalJournal article

Harvard

Gu, X, Angelov, PP, Kangin, D & Principe, J 2018, 'Self-organised direction aware data partitioning algorithm', Information Sciences, vol. 423, pp. 80-95.

APA

Gu, X., Angelov, P. P., Kangin, D., & Principe, J. (2018). Self-organised direction aware data partitioning algorithm. Information Sciences, 423, 80-95.

Vancouver

Gu X, Angelov PP, Kangin D, Principe J. Self-organised direction aware data partitioning algorithm. Information Sciences. 2018 Jan;423:80-95.

Author

Gu, Xiaowei ; Angelov, Plamen Parvanov ; Kangin, Dmitry ; Principe, Jose . / Self-organised direction aware data partitioning algorithm. In: Information Sciences. 2018 ; Vol. 423. pp. 80-95.

Bibtex

@article{24b43ef27df14f1bb76e4e25785db4a1,
title = "Self-organised direction aware data partitioning algorithm",
abstract = "In this paper, a novel fully data-driven algorithm, named Self-Organised Direction Aware (SODA), for data partitioning and forming data clouds is proposed. The proposed SODA algorithm employs an extra cosine similarity-based directional component to work together with a traditional distance metric, thus, takes the advantages of both the spatial and angular divergences. Using the nonparametric Empirical Data Analytics (EDA) operators, the proposed algorithm automatically identifies the main modes of the data pattern from the empirically observed data samples and uses them as focal points to form data clouds. A streaming data processing extension of the SODA algorithm is also proposed. This extension of the SODA algorithm is able to self-adjust the data clouds structure and parameters to follow the possibly changing data patterns and processes. Numerical examples provided as a proof of the concept testify the proposed algorithm as an autonomous algorithm and demonstrate its high clustering performance and computational efficiency.",
keywords = "Autonomous learning, Nonparametric, Clustering, Empirical Data Analytics (EDA), Cosine similarity , Traditional distance metric",
author = "Xiaowei Gu and Angelov, {Plamen Parvanov} and Dmitry Kangin 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, 423, 2018 DOI: 10.1016/j.ins.2017.09.025",
year = "2018",
month = jan
language = "English",
volume = "423",
pages = "80--95",
journal = "Information Sciences",
issn = "0020-0255",
publisher = "Elsevier Inc.",

}

RIS

TY - JOUR

T1 - Self-organised direction aware data partitioning algorithm

AU - Gu, Xiaowei

AU - Angelov, Plamen Parvanov

AU - Kangin, Dmitry

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, 423, 2018 DOI: 10.1016/j.ins.2017.09.025

PY - 2018/1

Y1 - 2018/1

N2 - In this paper, a novel fully data-driven algorithm, named Self-Organised Direction Aware (SODA), for data partitioning and forming data clouds is proposed. The proposed SODA algorithm employs an extra cosine similarity-based directional component to work together with a traditional distance metric, thus, takes the advantages of both the spatial and angular divergences. Using the nonparametric Empirical Data Analytics (EDA) operators, the proposed algorithm automatically identifies the main modes of the data pattern from the empirically observed data samples and uses them as focal points to form data clouds. A streaming data processing extension of the SODA algorithm is also proposed. This extension of the SODA algorithm is able to self-adjust the data clouds structure and parameters to follow the possibly changing data patterns and processes. Numerical examples provided as a proof of the concept testify the proposed algorithm as an autonomous algorithm and demonstrate its high clustering performance and computational efficiency.

AB - In this paper, a novel fully data-driven algorithm, named Self-Organised Direction Aware (SODA), for data partitioning and forming data clouds is proposed. The proposed SODA algorithm employs an extra cosine similarity-based directional component to work together with a traditional distance metric, thus, takes the advantages of both the spatial and angular divergences. Using the nonparametric Empirical Data Analytics (EDA) operators, the proposed algorithm automatically identifies the main modes of the data pattern from the empirically observed data samples and uses them as focal points to form data clouds. A streaming data processing extension of the SODA algorithm is also proposed. This extension of the SODA algorithm is able to self-adjust the data clouds structure and parameters to follow the possibly changing data patterns and processes. Numerical examples provided as a proof of the concept testify the proposed algorithm as an autonomous algorithm and demonstrate its high clustering performance and computational efficiency.

KW - Autonomous learning

KW - Nonparametric

KW - Clustering

KW - Empirical Data Analytics (EDA)

KW - Cosine similarity

KW - Traditional distance metric

M3 - Journal article

VL - 423

SP - 80

EP - 95

JO - Information Sciences

JF - Information Sciences

SN - 0020-0255

ER -