Verifying the proximity and size hypothesis for self-organizing maps

Persistent Link:
http://hdl.handle.net/10150/106111
Title:
Verifying the proximity and size hypothesis for self-organizing maps
Author:
Lin, Chienting; Chen, Hsinchun; Nunamaker, Jay F.
Citation:
Verifying the proximity and size hypothesis for self-organizing maps 2000-12, 16(3):57-70 Journal of Management Information Systems
Publisher:
M.E. Sharpe, Inc.
Journal:
Journal of Management Information Systems
Issue Date:
Dec-2000
Description:
Artificial Intelligence Lab, Department of MIS, University of Arizona
URI:
http://hdl.handle.net/10150/106111
Submitted date:
2004-09-04
Abstract:
The Kohonen Self-Organizing Map (SOM) is an unsupervised learning technique for summarizing high-dimensional data so that similar inputs are, in general, mapped close to one another. When applied to textual data, SOM has been shown to be able to group together related concepts in a data collection and to present major topics within the collection with larger regions. Research in which properties of SOM were validated, called the Proximity and Size Hypotheses,is presented through a user evaluation study. Building upon the previous research in automatic concept generation and classification, it is demonstrated that the Kohonen SOM was able to perform concept clustering effectively, based on its concept precision and recall7 scores as judged by human experts. A positive relationship between the size of an SOM region and the number of documents contained in the region is also demonstrated.
Type:
Journal Article (Paginated)
Language:
en
Keywords:
Management Information Systems; Knowledge Management; Information Systems
Local subject classification:
National Science Digital Library; NSDL; Artificial intelligence lab; AI lab; Document management; Decision support systems; Algorithms

Full metadata record

DC FieldValue Language
dc.contributor.authorLin, Chientingen_US
dc.contributor.authorChen, Hsinchunen_US
dc.contributor.authorNunamaker, Jay F.en_US
dc.date.accessioned2004-09-04T00:00:01Z-
dc.date.available2010-06-18T23:41:01Z-
dc.date.issued2000-12en_US
dc.date.submitted2004-09-04en_US
dc.identifier.citationVerifying the proximity and size hypothesis for self-organizing maps 2000-12, 16(3):57-70 Journal of Management Information Systemsen_US
dc.identifier.urihttp://hdl.handle.net/10150/106111-
dc.descriptionArtificial Intelligence Lab, Department of MIS, University of Arizonaen_US
dc.description.abstractThe Kohonen Self-Organizing Map (SOM) is an unsupervised learning technique for summarizing high-dimensional data so that similar inputs are, in general, mapped close to one another. When applied to textual data, SOM has been shown to be able to group together related concepts in a data collection and to present major topics within the collection with larger regions. Research in which properties of SOM were validated, called the Proximity and Size Hypotheses,is presented through a user evaluation study. Building upon the previous research in automatic concept generation and classification, it is demonstrated that the Kohonen SOM was able to perform concept clustering effectively, based on its concept precision and recall7 scores as judged by human experts. A positive relationship between the size of an SOM region and the number of documents contained in the region is also demonstrated.en_US
dc.format.mimetypetext/htmlen_US
dc.language.isoenen_US
dc.publisherM.E. Sharpe, Inc.en_US
dc.subjectManagement Information Systemsen_US
dc.subjectKnowledge Managementen_US
dc.subjectInformation Systemsen_US
dc.subject.otherNational Science Digital Libraryen_US
dc.subject.otherNSDLen_US
dc.subject.otherArtificial intelligence laben_US
dc.subject.otherAI laben_US
dc.subject.otherDocument managementen_US
dc.subject.otherDecision support systemsen_US
dc.subject.otherAlgorithmsen_US
dc.titleVerifying the proximity and size hypothesis for self-organizing mapsen_US
dc.typeJournal Article (Paginated)en_US
dc.identifier.journalJournal of Management Information Systemsen_US
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