Skip to main content

Self-Organised direction aware data partitioning algorithm

Gu, Xiaowei, Angelov, Plamen, Kangin, Dmitry, Principe, Jose (2018) Self-Organised direction aware data partitioning algorithm. Information Sciences, 423 . pp. 80-95. ISSN 0020-0255. (doi:10.1016/j.ins.2017.09.025) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:90207)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1016/j.ins.2017.09.025

Abstract

In this paper, a novel fully data-driven algorithm, named Self-Organised Direction Aware (SODA) 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 illustrate the proposed algorithm as an autonomous algorithm and demonstrate its high clustering performance and computational efficiency.

Item Type: Article
DOI/Identification number: 10.1016/j.ins.2017.09.025
Uncontrolled keywords: Autonomous learning; Nonparametric; Clustering; Empirical Data Analytics (EDA); Cosine similarity; Traditional distance metric
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Amy Boaler
Date Deposited: 14 Sep 2021 13:20 UTC
Last Modified: 15 Sep 2021 14:57 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/90207 (The current URI for this page, for reference purposes)
  • Depositors only (login required):