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A Modified Fuzzy Clustering for Documents Retrieval: Application to Document Categorization

Nefti-Meziani, S., Oussalah, M. and Rezgui, Yacine ORCID: 2009. A Modified Fuzzy Clustering for Documents Retrieval: Application to Document Categorization. Journal of the Operational Research Society 60 (3) , pp. 384-394. 10.1057/palgrave.jors.2602555

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
Uncontrolled Keywords: fuzzy clustering, document clustering, fuzzy decision making
Publisher: Palgrave Macmillan
ISSN: 0160-5682
Last Modified: 17 Oct 2022 09:44

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