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