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An Alternative Extension of the FCM Algorithm for Clustering Fuzzy Databases

Authors: Amel Grissa Touzi;

An Alternative Extension of the FCM Algorithm for Clustering Fuzzy Databases

Abstract

Several real applications need to manage fuzzy information. Among the languages proposed for this type of data, the Fuzzy SQL (FSQL) language had a great success, seen its great power of modeling and it’s an extension of the well-known SQL language. In this paper, we propose an alternative for FCM algorithm For Fuzzy Database describe with FSQL. The conventional fuzzy clustering algorithms form fuzzy clusters so as to minimize the total distance from cluster centers to data points. However, they cannot be applied in the case where the data vectors are described with FSQL is given. To concretize our approach we used the BDRF described with the GEFRED model, which is supporting the FSQL language.

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Powered by OpenAIRE graph
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
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