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A modified fuzzy clustering for documents retrieval: application to document categorization

Author

Listed:
  • S Nefti

    (University of Salford)

  • M Oussalah

    (University of Birmingham)

  • Y Rezgui

    (University of Salford)

Abstract

The paper advocates the use of a new fuzzy-based clustering algorithm for document categorization. Each document/datum will be represented as a fuzzy set. In this respect, the fuzzy clustering algorithm, will be constrained additionally in order to cluster fuzzy sets. Then, one needs to find a metric measure in order to detect the overlapping between documents and the cluster prototype (category). In this respect, we use one of the interclass probabilistic reparability measures known as Bhattacharyya distance, which will be incorporated in the general scheme of the fuzzy c-means algorithm for measuring the overlapping between fuzzy sets. This enables the introduction of fuzziness in the document clustering in the sense that it allows a single document to belong to more than one category. This is in line with semantic multiple interpretations conveyed by single words, which support multiple membership to several classes. Performances of the algorithms will be illustrated using a case study from the construction sector.

Suggested Citation

  • S Nefti & M Oussalah & Y Rezgui, 2009. "A modified fuzzy clustering for documents retrieval: application to document categorization," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(3), pages 384-394, March.
    • Handle: RePEc:pal:jorsoc:v:60:y:2009:i:3:d:10.1057_palgrave.jors.2602555
    DOI: 10.1057/palgrave.jors.2602555
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    Cited by:

    1. Na Xu & Xueqing Zhou & Chaoran Guo & Bai Xiao & Fei Wei & Yuting Hu, 2022. "Text Mining Applications in the Construction Industry: Current Status, Research Gaps, and Prospects," Sustainability, MDPI, vol. 14(24), pages 1-29, December.

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