Conceptual clustering in information retrieval

Sanjiv K. Bhatia, Jitender S. Deogun

Research output: Contribution to journalArticle

61 Citations (Scopus)

Abstract

Clustering is used 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 associated with each other are assigned to the same cluster. This association is generally determined by examining the index term representation of documents or by capturing user feedback on queries on 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 a cluster representation that can be used during query as well as to assign new documents to the appropriate clusters.

Original languageEnglish (US)
Pages (from-to)427-436
Number of pages10
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Volume28
Issue number3
DOIs
StatePublished - Dec 1 1998

Fingerprint

Information retrieval
Information retrieval systems
Knowledge acquisition
Feedback

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Conceptual clustering in information retrieval. / Bhatia, Sanjiv K.; Deogun, Jitender S.

In: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, Vol. 28, No. 3, 01.12.1998, p. 427-436.

Research output: Contribution to journalArticle

@article{522b88b0da62455d82b73dd792563d73,
title = "Conceptual clustering in information retrieval",
abstract = "Clustering is used 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 associated with each other are assigned to the same cluster. This association is generally determined by examining the index term representation of documents or by capturing user feedback on queries on 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 a cluster representation that can be used during query as well as to assign new documents to the appropriate clusters.",
author = "Bhatia, {Sanjiv K.} and Deogun, {Jitender S.}",
year = "1998",
month = "12",
day = "1",
doi = "10.1109/3477.678640",
language = "English (US)",
volume = "28",
pages = "427--436",
journal = "IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics",
issn = "1083-4419",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "3",

}

TY - JOUR

T1 - Conceptual clustering in information retrieval

AU - Bhatia, Sanjiv K.

AU - Deogun, Jitender S.

PY - 1998/12/1

Y1 - 1998/12/1

N2 - Clustering is used 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 associated with each other are assigned to the same cluster. This association is generally determined by examining the index term representation of documents or by capturing user feedback on queries on 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 a cluster representation that can be used during query as well as to assign new documents to the appropriate clusters.

AB - Clustering is used 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 associated with each other are assigned to the same cluster. This association is generally determined by examining the index term representation of documents or by capturing user feedback on queries on 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 a cluster representation that can be used during query as well as to assign new documents to the appropriate clusters.

UR - http://www.scopus.com/inward/record.url?scp=0032099961&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0032099961&partnerID=8YFLogxK

U2 - 10.1109/3477.678640

DO - 10.1109/3477.678640

M3 - Article

C2 - 18255959

AN - SCOPUS:0032099961

VL - 28

SP - 427

EP - 436

JO - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

JF - IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

SN - 1083-4419

IS - 3

ER -