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A Novel Fuzzy-Connectedness-Based Incremental Clustering Algorithm for Large Databases

机译:基于模糊连接的大型数据库增量聚类算法

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Many clustering methods have been proposed in data mining fields, but seldom were focused on the incremental databases. In this paper, we present an incremental algorithm-IFHC that is applicable in periodically incremental environment based on FHC. Not only can FHC and IFHC dispose the data with numeric attributes, but with categorical attributes. Experiment shows that IFHC is faster and more efficient than FHC in update of databases.
机译:在数据挖掘字段中提出了许多聚类方法,但很少集中在增量数据库上。在本文中,我们介绍了一种增量算法-IFHC,适用于基于FHC的周期性增量环境。 FHC和IFHC不仅可以使用数字属性将数据放置,但具有分类属性。实验表明,IFHC在数据库的更新中比FHC更快且更效率。

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