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.
Keywords
Computer scienceInformation retrievalConstruct (python library)Cluster analysisProcess (computing)Document clusteringCluster (spacecraft)Representation (politics)Data miningArtificial intelligence
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Publication Info
- Year
- 1998
- Type
- article
- Volume
- 28
- Issue
- 3
- Pages
- 427-436
- Citations
- 84
- Access
- Closed
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Cite This
Sanjiv Bhatia,
Jitender S. Deogun
(1998).
Conceptual clustering in information retrieval.
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)
, 28
(3)
, 427-436.
https://doi.org/10.1109/3477.678640
Identifiers
- DOI
- 10.1109/3477.678640