View in EDS

CQoCO: A measure for comparative quality of coverage and organization for self-organizing maps

Saved in:
Bibliographic Details
Title: CQoCO: A measure for comparative quality of coverage and organization for self-organizing maps
Authors: Beaton, Derek1, Valova, Iren ivalova@umassd.edu, MacLean, Daniel1
Source: Neurocomputing. Jun2010, Vol. 73 Issue 10-12, p2147-2159. 13p.
Subjects: Self-organizing maps, Comparative studies, Geometric quantization, Error analysis in mathematics, Artificial neural networks, Cluster analysis (Statistics)
Abstract: Abstract: In this paper we introduce a comprehensive measure of quality of coverage and organization in self-organizing maps. The new measure, named CQoCO, combines self-organizing criteria as outlined by Polani, by measuring how, and where neurons are mapped in space and how they are organized. The result is an all-inclusive tool to account for the required properties of a well-organized SOM—untangled coverage of inputs only, reflecting the density of the input space and outlining the natural clusters in the data. The proposed measure has been tested on the traditional version of SOM as well as on a growing version, as proposed by the authors—ParaSOM. While the patterns are simple, the tests also cover a comparison between CQoCO and the measures of quantization error and topological error. The conclusion becomes evident with the progress of the experiments—CQoCO provides an across-the-board measurement of the quality of coverage and organization in SOMs. [Copyright &y& Elsevier]
Copyright of Neurocomputing 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: 50391967
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: CQoCO: A measure for comparative quality of coverage and organization for self-organizing maps
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Beaton%2C+Derek%22">Beaton, Derek</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Valova%2C+Iren%22">Valova, Iren</searchLink><i> ivalova@umassd.edu</i><br /><searchLink fieldCode="AR" term="%22MacLean%2C+Daniel%22">MacLean, Daniel</searchLink><relatesTo>1</relatesTo>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Neurocomputing%22">Neurocomputing</searchLink>. Jun2010, Vol. 73 Issue 10-12, p2147-2159. 13p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Self-organizing+maps%22">Self-organizing maps</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br /><searchLink fieldCode="DE" term="%22Geometric+quantization%22">Geometric quantization</searchLink><br /><searchLink fieldCode="DE" term="%22Error+analysis+in+mathematics%22">Error analysis in mathematics</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Abstract: In this paper we introduce a comprehensive measure of quality of coverage and organization in self-organizing maps. The new measure, named CQoCO, combines self-organizing criteria as outlined by Polani, by measuring how, and where neurons are mapped in space and how they are organized. The result is an all-inclusive tool to account for the required properties of a well-organized SOM—untangled coverage of inputs only, reflecting the density of the input space and outlining the natural clusters in the data. The proposed measure has been tested on the traditional version of SOM as well as on a growing version, as proposed by the authors—ParaSOM. While the patterns are simple, the tests also cover a comparison between CQoCO and the measures of quantization error and topological error. The conclusion becomes evident with the progress of the experiments—CQoCO provides an across-the-board measurement of the quality of coverage and organization in SOMs. [Copyright &y& Elsevier]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Neurocomputing 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=50391967
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.neucom.2010.02.004
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 2147
    Subjects:
      – SubjectFull: Self-organizing maps
        Type: general
      – SubjectFull: Comparative studies
        Type: general
      – SubjectFull: Geometric quantization
        Type: general
      – SubjectFull: Error analysis in mathematics
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Cluster analysis (Statistics)
        Type: general
    Titles:
      – TitleFull: CQoCO: A measure for comparative quality of coverage and organization for self-organizing maps
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Beaton, Derek
      – PersonEntity:
          Name:
            NameFull: Valova, Iren
      – PersonEntity:
          Name:
            NameFull: MacLean, Daniel
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 06
              Text: Jun2010
              Type: published
              Y: 2010
          Identifiers:
            – Type: issn-print
              Value: 09252312
          Numbering:
            – Type: volume
              Value: 73
            – Type: issue
              Value: 10-12
          Titles:
            – TitleFull: Neurocomputing
              Type: main
ResultId 1