模糊数据库聚类中FCM算法的另一种扩展

2010 Second International Conference on Advances in Databases, Knowledge, and Data Applications Pub Date : 2010-04-11 DOI:10.1109/DBKDA.2010.35

A. Touzi

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引用次数: 9

摘要

一些实际应用需要管理模糊信息。在针对这类数据提出的语言中，模糊SQL (FSQL)语言取得了巨大的成功，因为它具有强大的建模能力，而且它是众所周知的SQL语言的扩展。本文针对用FSQL描述的模糊数据库，提出了一种替代FCM算法的方法。传统的模糊聚类算法形成模糊聚类是为了使聚类中心到数据点的总距离最小。然而，它们不能应用于用FSQL描述数据向量的情况。为了使我们的方法具体化，我们使用了GEFRED模型描述的BDRF，它支持FSQL语言。

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An Alternative Extension of the FCM Algorithm for Clustering Fuzzy Databases

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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2010 Second International Conference on Advances in Databases, Knowledge, and Data Applications

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