View in EDS

Self-Organised direction aware data partitioning algorithm.

Saved in:
Bibliographic Details
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]
Copyright of Information Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 125722740
AccessLevel: 6
PubType: Periodical
PubTypeId: serialPeriodical
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Self-Organised direction aware data partitioning algorithm.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Gu%2C+Xiaowei%22">Gu, Xiaowei</searchLink><relatesTo>1</relatesTo><i> x.gu3@lancaster.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Angelov%2C+Plamen%22">Angelov, Plamen</searchLink><relatesTo>1,2</relatesTo><i> p.angelov@lancaster.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Kangin%2C+Dmitry%22">Kangin, Dmitry</searchLink><relatesTo>1</relatesTo><i> dkangin@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Principe%2C+Jose%22">Principe, Jose</searchLink><relatesTo>3</relatesTo><i> principe@cnel.ufl.edu</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Information+Sciences%22">Information Sciences</searchLink>. Jan2018, Vol. 423, p80-95. 16p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Natural+language+processing%22">Natural language processing</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+data+processing+management%22">Electronic data processing management</searchLink><br /><searchLink fieldCode="DE" term="%22Cosine+function%22">Cosine function</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+algorithms%22">Parallel algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Euclidean+algorithm%22">Euclidean algorithm</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Information Sciences is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=125722740
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.ins.2017.09.025
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 80
    Subjects:
      – SubjectFull: Natural language processing
        Type: general
      – SubjectFull: Electronic data processing management
        Type: general
      – SubjectFull: Cosine function
        Type: general
      – SubjectFull: Parallel algorithms
        Type: general
      – SubjectFull: Euclidean algorithm
        Type: general
    Titles:
      – TitleFull: Self-Organised direction aware data partitioning algorithm.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Gu, Xiaowei
      – PersonEntity:
          Name:
            NameFull: Angelov, Plamen
      – PersonEntity:
          Name:
            NameFull: Kangin, Dmitry
      – PersonEntity:
          Name:
            NameFull: Principe, Jose
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 01
              Text: Jan2018
              Type: published
              Y: 2018
          Identifiers:
            – Type: issn-print
              Value: 00200255
          Numbering:
            – Type: volume
              Value: 423
          Titles:
            – TitleFull: Information Sciences
              Type: main
ResultId 1