Cluster characterization in information retrieval

Sanjiv K. Bhatia, Jitender S. Deogun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Clustering is employed in information retrieval systems to enhance the efficiency and effectiveness of the retrieval process. Clustering is achieved by partitioning the documents in a collection into classes such that documents that are jointly relevant to some topics are assigned to the same cluster. This relevance can be determined by examining the index term representation of documents or by capturing user feedback on queries to the system. In cluster-oriented systems, the retrieval process can be enhanced by employing characterization of clusters. In this paper, we present the techniques to develop clusters and cluster characterizations by employing user viewpoint. The user viewpoint is elicited through a structured interview based on a knowledge acquisition technique, namely Personal Construct Theory. It is demonstrated that the application of personal construct theory results in an adequate representation of clusters that can be used during query as well as to assign new documents to the appropriate clusters.

Original languageEnglish (US)
Title of host publicationProceedings of the 1993 ACM/SIGAPP Symposium on Applied Computing
Subtitle of host publicationStates of the Art and Practice, SAC 1993
EditorsEd Deaton, George Hedrick, K.M. George, Hal Berghel
PublisherAssociation for Computing Machinery
Pages721-728
Number of pages8
ISBN (Electronic)0897915674
DOIs
StatePublished - Mar 1 1993
Event1993 ACM/SIGAPP Symposium on Applied Computing: States of the Art and Practice, SAC 1993 - Indianapolis, United States
Duration: Feb 14 1993Feb 16 1993

Publication series

NameProceedings of the ACM Symposium on Applied Computing
VolumePart F129680

Other

Other1993 ACM/SIGAPP Symposium on Applied Computing: States of the Art and Practice, SAC 1993
CountryUnited States
CityIndianapolis
Period2/14/932/16/93

Keywords

  • Cluster characterization
  • Document clustering
  • Learning in information retrieval
  • Text analysis

ASJC Scopus subject areas

  • Software

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