Paper Info
Title
A modified fuzzy clustering for documents retrieval: application to document categorization
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.
Year
DOI
Venue
2009
10.1057/palgrave.jors.2602555
JORS
Keywords
Field
DocType
operational research,fuzzy clustering,scheduling,communications technology,location,marketing,investment,project management,inventory,information technology,management science,computer science,document retrieval,operations research,reliability,forecasting,information systems,logistics,production
Data mining,Fuzzy clustering,Fuzzy classification,Defuzzification,Computer science,Document clustering,Fuzzy set operations,Fuzzy set,Artificial intelligence,Type-2 fuzzy sets and systems,Fuzzy number,Machine learning
Journal
Volume
Issue
ISSN
60
3
0160-5682
Citations 
PageRank 
References 
8
0.53
8
Authors
3
Name
Order
Citations
PageRank
Samia Nefti18810.68
Mourad Oussalah234476.14
Yacine Rezgui337945.97