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Self-Organised direction aware data partitioning algorithm.

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Title: Self-Organised direction aware data partitioning algorithm.
Authors: Gu, Xiaowei1 x.gu3@lancaster.ac.uk, Angelov, Plamen1,2 p.angelov@lancaster.ac.uk, Kangin, Dmitry1 dkangin@gmail.com, Principe, Jose3 principe@cnel.ufl.edu
Source: Information Sciences. Jan2018, Vol. 423, p80-95. 16p.
Subjects: Natural language processing, Electronic data processing management, Cosine function, Parallel algorithms, Euclidean algorithm
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. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
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. [ABSTRACT FROM AUTHOR]
ISSN:00200255
DOI:10.1016/j.ins.2017.09.025