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Self-organised direction aware data partitioning algorithm

Research output: Contribution to journalJournal article

<mark>Journal publication date</mark>01/2018
<mark>Journal</mark>Information Sciences
Number of pages16
Pages (from-to)80-95
Early online date19/09/17
<mark>Original language</mark>English


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.

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